2015Applied Vegetation ScienceRequires access

Spatial variation of fuel loading within varying aged stands of chaparral

Kellie A. Uyeda, Douglas A. Stow, John F. O’Leary, Ian T. Schmidt, Philip J. Riggan

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Abstract

Abstract Questions How do stand‐level biomass and percentage of dead material in chaparral vary as a function of stand age? How do the landscape properties of aggregation index and patch size vary in each of the dominant species groups as a function of stand age? Location Stands of 7‐, 28‐ and 68‐yr‐old chaparral, San Diego County, CA, US. Methods We sampled and estimated above‐ground biomass in 8 m × 8 m field plots. Each stand was classified into species groups using high‐resolution imagery. After establishing coefficients for the average biomass per area for each species group and stand age using field data, we estimated biomass in 30 random plots in each stand age using the classified map. We also estimated biomass using pooled coefficients (average of the 28‐ and 68‐yr‐old biomass per area). We calculated landscape metrics in six 60 m × 60 m study sites for each stand age. Results While we found a statistically significant pattern of higher biomass in the older chaparral stands when using age‐specific biomass coefficients, there was no statistical difference between the 28‐ and 68‐yr‐old stands using the more conservative pooled coefficients. Both approaches revealed a wide range in biomass in all age classes. We were not able to map dead biomass with sufficient accuracy to measure age‐related differences, but no differences in the two older stands were obvious in the field. There were no significant differences in landscape metrics between the two older stands, and the differences observed in the younger stand might have been partially due to aspect differences. Conclusions Our approach of using a combination of field plots and classification of remote sensing imagery is a valuable method for enlarging the effective study area in chaparral, and allowed us to better measure how widely biomass varies across the study area. High spatial resolution measurements of fuel properties could help support more detailed models of fire behaviour.

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Abstract Questions How do stand‐level biomass and percentage of dead material in chaparral vary as a function of stand age? How do the landscape properties of aggregation index and patch size vary in each of the dominant species groups as a function of stand age? Location Stands of 7‐, 28‐ and 68‐yr‐old chaparral, San Diego County, CA, US. Methods We sampled and estimated above‐ground biomass in 8 m × 8 m field plots. Each stand was classified into species groups using high‐resolution imagery. After establishing coefficients for the average biomass per area for each species group and stand age using field data, we estimated biomass in 30 random plots in each stand age using the classified map. We also estimated biomass using pooled coefficients (average of the 28‐ and 68‐yr‐old biomass per area). We calculated landscape metrics in six 60 m × 60 m study sites for each stand age. Results While we found a statistically significant pattern of higher biomass in the older chaparral stands when using age‐specific biomass coefficients, there was no statistical difference between the 28‐ and 68‐yr‐old stands using the more conservative pooled coefficients. Both approaches revealed a wide range in biomass in all age classes. We were not able to map dead biomass with sufficient accuracy to measure age‐related differences, but no differences in the two older stands were obvious in the field. There were no significant differences in landscape metrics between the two older stands, and the differences observed in the younger stand might have been partially due to aspect differences. Conclusions Our approach of using a combination of field plots and classification of remote sensing imagery is a valuable method for enlarging the effective study area in chaparral, and allowed us to better measure how widely biomass varies across the study area. High spatial resolution measurements of fuel properties could help support more detailed models of fire behaviour.

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Available abstract

Abstract Questions How do stand‐level biomass and percentage of dead material in chaparral vary as a function of stand age? How do the landscape properties of aggregation index and patch size vary in each of the dominant species groups as a function of stand age? Location Stands of 7‐, 28‐ and 68‐yr‐old chaparral, San Diego County, CA, US. Methods We sampled and estimated above‐ground biomass in 8 m × 8 m field plots. Each stand was classified into species groups using high‐resolution imagery. After establishing coefficients for the average biomass per area for each species group and stand age using field data, we estimated biomass in 30 random plots in each stand age using the classified map. We also estimated biomass using pooled coefficients (average of the 28‐ and 68‐yr‐old biomass per area). We calculated landscape metrics in six 60 m × 60 m study sites for each stand age. Results While we found a statistically significant pattern of higher biomass in the older chaparral stands when using age‐specific biomass coefficients, there was no statistical difference between the 28‐ and 68‐yr‐old stands using the more conservative pooled coefficients. Both approaches revealed a wide range in biomass in all age classes. We were not able to map dead biomass with sufficient accuracy to measure age‐related differences, but no differences in the two older stands were obvious in the field. There were no significant differences in landscape metrics between the two older stands, and the differences observed in the younger stand might have been partially due to aspect differences. Conclusions Our approach of using a combination of field plots and classification of remote sensing imagery is a valuable method for enlarging the effective study area in chaparral, and allowed us to better measure how widely biomass varies across the study area. High spatial resolution measurements of fuel properties could help support more detailed models of fire behaviour.

Key concepts: Chaparral, Biomass (ecology), Range (aeronautics), Geography, Environmental science, Physical geography, Ecology, Forestry

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