DOI: 10.1016/j.foreco.2017.03.001
Scopus记录号: 2-s2.0-85014802670
论文题名: Improving forest sampling strategies for assessment of fuel reduction burning
作者: Gharun M. ; Possell M. ; Jenkins M.E. ; Poon L.F. ; Bell T.L. ; Adams M.A.
刊名: Forest Ecology and Management
ISSN: 0378-1127
出版年: 2017
卷: 392 起始页码: 78
结束页码: 89
语种: 英语
英文关键词: Biomass
; Bushfires
; Forest fuel
; Landsat 8
; Prescribed burning
; Soil carbon
; Soil nitrogen
; Spatial analysis
; Stratified sampling
Scopus关键词: Biomass
; Forestry
; Managers
; Nitrogen
; Soil testing
; Soils
; Vegetation
; Bushfires
; Forest fuel
; LANDSAT
; Prescribed burning
; Soil carbon
; Soil nitrogen
; Spatial analysis
; Stratified sampling
; Fuels
英文摘要: Land managers typically make post hoc assessments of the effectiveness of fuel reduction burning (FRB), but often lack a rigorous sampling framework. A general, but untested, assumption is that variability in soil and fuel properties increases from small (∼1 m) to large spatial scales (∼10–100 km). Based on a recently published field-based sampling scheme, we addressed the following questions: (i) How much variability is captured in measurements collected at different spatial scales? (ii) What is the optimal number of sampling plots required for statistically robust characterisation of burnt areas? (iii) How can land managers improve their assessment of the effectiveness of FRB? We found that measurement variability does not increase with scale for all fuel components. Results showed that coarse woody debris is as variable at the small scale (plot, m) as it is at the landscape scale (km). For certain fuel components, such as litter biomass (in unburnt areas), overstorey biomass and leaf area, and soil properties such as total carbon and total nitrogen, samples taken at the small (plot) scale were indicative of variation at the larger scale of an individual FRB and more broadly across the landscape. We then tested the hypothesis that site stratification can reduce variability between sampling plots and as a consequence will reduce the required number of sampling plots. To test this hypothesis we used Landsat Normalized Difference Vegetation Index (NDVI) across areas treated with FRB and compared the number of sampling plots required to estimate mean fuel biomass with and without stratification. Stratification of burnt areas using remotely sensed vegetation indices reduced the number of sampling plots required. We provide a model of green biomass from Landsat NDVI and make recommendations on how sampling schemes can be improved for assessment of fuel reduction burning. © 2017 Elsevier B.V. Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/64384
Appears in Collections: 影响、适应和脆弱性
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作者单位: Centre for Carbon, Water and Food, School of Life and Environmental Sciences, University of Sydney, Sydney, NSW, Australia; Bushfire and Natural Hazards Cooperative Research Centre, East Melbourne, VIC, Australia; Centre for Environmental Risk Management of Bushfires, School of Biology, Faculty of Science, Health and Medicine, University of Wollongong, Wollongong, NSW, Australia
Recommended Citation:
Gharun M.,Possell M.,Jenkins M.E.,et al. Improving forest sampling strategies for assessment of fuel reduction burning[J]. Forest Ecology and Management,2017-01-01,392