Comparison of regression coefficient and GIS-based methodologies for regional estimates of forest soil carbon stocks.
Environ Pollut
; 152(2): 267-73, 2008 Mar.
Article
en En
| MEDLINE
| ID: mdl-17706329
ABSTRACT
Estimates of forest soil organic carbon (SOC) have applications in carbon science, soil quality studies, carbon sequestration technologies, and carbon trading. Forest SOC has been modeled using a regression coefficient methodology that applies mean SOC densities (mass/area) to broad forest regions. A higher resolution model is based on an approach that employs a geographic information system (GIS) with soil databases and satellite-derived landcover images. Despite this advancement, the regression approach remains the basis of current state and federal level greenhouse gas inventories. Both approaches are analyzed in detail for Wisconsin forest soils from 1983 to 2001, applying rigorous error-fixing algorithms to soil databases. Resulting SOC stock estimates are 20% larger when determined using the GIS method rather than the regression approach. Average annual rates of increase in SOC stocks are 3.6 and 1.0 million metric tons of carbon per year for the GIS and regression approaches respectively.
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Banco de datos:
MEDLINE
Asunto principal:
Suelo
/
Carbono
/
Monitoreo del Ambiente
/
Modelos Estadísticos
/
Efecto Invernadero
/
Sistemas de Información Geográfica
Tipo de estudio:
Diagnostic_studies
/
Risk_factors_studies
Idioma:
En
Año:
2008
Tipo del documento:
Article