IrulappaPillaiVijayakumar2016

Référence

Irulappa Pillai Vijayakumar, D.B. (2016) Hybrid modeling of aboveground biomass carbon using disturbance history over large areas of boreal forest in eastern Canada. Thèse de doctorat, Université Laval. (URL )

Résumé

Fire is as a main succession driver in northeastern American boreal forests and time since last fire (TSLF) is seen as a useful covariate to infer the spatial variation of carbon. The first two objectives of this thesis are: (1) to elaborate a TSLF map over an extensive region in boreal forests of eastern Canada (217,000 km2) and (2) to predict aboveground carbon biomass (ABC) as a function of TSLF at a scale related to fire disturbances. A non-parametric model was first developed to predict TSLF using historical records of fire, forest inventory data and climate data at a 2-km2 scale. Two kilometer square is the minimum size for fires to be considered important enough and included in the Canadian large fire database. Overall, we found a substantial agreement at the scale of both the study area and landscape units, but the accuracy remained fairly low at the scale of individual 2-km2 cells. A hierarchical modeling approach is then presented for scaling-up ABC from inventory plots to the same 2 km2 scale. The proportions of cover density classes were the most important variables to predict ABC. ABC was also related to the speed of post-fire canopy recovery through which TSLF acts indirectly upon ABC. Finally, we compared remote sensing based aboveground biomass estimates with our inventory based estimates to provide insights on improving their accuracy. The results indicated again that abundances of canopy cover density classes of surficial deposits, and TSLF may serve as ancillary variables for improving substantially the accuracy of remotely sensed biomass estimates. The study results have shown: 1) the importance of lengthening the historical records of fire records to provide a better perspective of the actual changes of fire regime; 2) the importance of incorporating post-fire canopy recovery information together with ABC yield curves in carbon budget models at a spatial scale related to fire disturbances; 3) the importance of adding disturbance history and vegetation recovery trends with remote sensing reflectance data to improve accuracy for biomass mapping.

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@PHDTHESIS { IrulappaPillaiVijayakumar2016,
    TITLE = { Hybrid modeling of aboveground biomass carbon using disturbance history over large areas of boreal forest in eastern Canada },
    AUTHOR = { Irulappa Pillai Vijayakumar, D.B. },
    SCHOOL = { Université Laval },
    YEAR = { 2016 },
    NOTE = { CEFTMS, Raulier, F. },
    ABSTRACT = { Fire is as a main succession driver in northeastern American boreal forests and time since last fire (TSLF) is seen as a useful covariate to infer the spatial variation of carbon. The first two objectives of this thesis are: (1) to elaborate a TSLF map over an extensive region in boreal forests of eastern Canada (217,000 km2) and (2) to predict aboveground carbon biomass (ABC) as a function of TSLF at a scale related to fire disturbances. A non-parametric model was first developed to predict TSLF using historical records of fire, forest inventory data and climate data at a 2-km2 scale. Two kilometer square is the minimum size for fires to be considered important enough and included in the Canadian large fire database. Overall, we found a substantial agreement at the scale of both the study area and landscape units, but the accuracy remained fairly low at the scale of individual 2-km2 cells. A hierarchical modeling approach is then presented for scaling-up ABC from inventory plots to the same 2 km2 scale. The proportions of cover density classes were the most important variables to predict ABC. ABC was also related to the speed of post-fire canopy recovery through which TSLF acts indirectly upon ABC. Finally, we compared remote sensing based aboveground biomass estimates with our inventory based estimates to provide insights on improving their accuracy. The results indicated again that abundances of canopy cover density classes of surficial deposits, and TSLF may serve as ancillary variables for improving substantially the accuracy of remotely sensed biomass estimates. The study results have shown: 1) the importance of lengthening the historical records of fire records to provide a better perspective of the actual changes of fire regime; 2) the importance of incorporating post-fire canopy recovery information together with ABC yield curves in carbon budget models at a spatial scale related to fire disturbances; 3) the importance of adding disturbance history and vegetation recovery trends with remote sensing reflectance data to improve accuracy for biomass mapping. },
    URL = { https://corpus.ulaval.ca/jspui/handle/20.500.11794/26572 },
    TIMESTAMP = { 2019-10-09 },
}

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