DOI: 10.1016/j.foreco.2015.08.027
Scopus记录号: 2-s2.0-84941585421
论文题名: Remote sensing proxies of productivity and moisture predict forest stand type and recovery rate following experimental harvest
作者: Nijland W. ; Coops N.C. ; Macdonald S.E. ; Nielsen S.E. ; Bater C.W. ; White B. ; Ogilvie J. ; Stadt J.
刊名: Forest Ecology and Management
ISSN: 0378-1127
出版年: 2015
卷: 357 起始页码: 239
结束页码: 247
语种: 英语
英文关键词: ALS
; Depth to water
; Edatopic grid
; EMEND
; Landsat
; Lidar
Scopus关键词: Computer vision
; Ecology
; Moisture
; Optical radar
; Productivity
; Remote sensing
; Vegetation
; ALS
; Depth-to-water
; Edatopic grid
; EMEND
; LANDSAT
; Forestry
; biological production
; boreal forest
; cartography
; coniferous forest
; deciduous forest
; elevation
; forest cover
; harvesting
; image analysis
; Landsat
; laser method
; lidar
; moisture content
; prediction
; remote sensing
; satellite imagery
; stand structure
; vegetation structure
; Classification
; Ecology
; Forests
; Information Retrieval
; Moisture
; Productivity
; Remote Sensing
; Alberta
; Canada
; Coniferophyta
英文摘要: Site productivity, as affected by soil nutrients and available moisture is often characterized using an edatopic grid. A challenge for forest ecologists and managers working across large areas and in complex landscapes is the need to identify spatially different ecological environments that follow an edatopic classification. Recent advances in remote sensing offer some potential approaches for mapping ecological environments and landscape conditions. It is now feasible to compile long temporal image sequences using Landsat imagery for reconstruction of forest stands and derivation of long term indices of landscape productivity; and the increasing proliferation of airborne laser scanning (ALS) technology also allows for the acquisition of detailed information on topographic elevation and vegetation structure with sub-meter accuracy. In a large area of boreal mixedwood forest in northwestern Alberta, Canada, we examined the utility of using Landsat-derived vegetation greenness (indicating productivity), and ALS-derived cartographic depth-to-water (indicating moisture), to determine forest cover type and vegetation responses following variable retention harvesting. Our results demonstrate that both long-term image sequences from Landsat and ALS-derived topography and vegetation structure act as proxies for edatopic grid components, and are well-suited to differentiating forest cover types. Deciduous-dominated, deciduous-dominated with conifer understory, mixed, and conifer-dominated forests were generally distributed (in order) across a gradient of increasing moisture and decreasing greenness. Landscape greenness was the strongest predictor of vegetation regrowth after disturbance, followed by depth to water and other terrain factors, such as elevation and slope. New advances in, and complementary use of, different remotely sensed information provides a better understanding of both the landscape-scale distribution of forest cover types and patterns of vegetation regrowth following disturbance. © 2015 Elsevier B.V. Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/65281
Appears in Collections: 影响、适应和脆弱性
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作者单位: Department of Forest Resources Management, University of British Columbia, 2424 Main Mall, Vancouver, BC, Canada; Department of Renewable Resources, University of Alberta, Edmonton, Canada; Forest Management Branch, Forestry Division, Alberta Agriculture and Forestry, 9920-108 Street, Edmonton, AB, Canada; Faculty of Forestry and Environmental Management, University of New Brunswick, 28 Dineen Drive Fredericton, New Brunswick, Canada
Recommended Citation:
Nijland W.,Coops N.C.,Macdonald S.E.,et al. Remote sensing proxies of productivity and moisture predict forest stand type and recovery rate following experimental harvest[J]. Forest Ecology and Management,2015-01-01,357