DOI: 10.1016/j.foreco.2013.07.059
Scopus记录号: 2-s2.0-84883503642
论文题名: Combining ensemble modeling and remote sensing for mapping individual tree species at high spatial resolution
作者: Engler R. ; Waser L.T. ; Zimmermann N.E. ; Schaub M. ; Berdos S. ; Ginzler C. ; Psomas A.
刊名: Forest Ecology and Management
ISSN: 0378-1127
出版年: 2013
卷: 310 起始页码: 64
结束页码: 73
语种: 英语
英文关键词: Aerial imagery
; Ensemble forecast
; Forest ecosystems
; Species distribution modeling
; Switzerland
; Vegetation mapping
Scopus关键词: Aerial imagery
; Ensemble forecasts
; Forest ecosystem
; Species distribution modeling
; Switzerland
; Vegetation mapping
; Aerial photography
; Image resolution
; Mapping
; Population distribution
; Remote sensing
; Uncertainty analysis
; Vegetation
; Forestry
; classification
; climate variation
; data quality
; forest management
; remote sensing
; spatial resolution
; species diversity
; statistical analysis
; tree
; Air Craft
; Ecosystems
; Image Analysis
; Mapping
; Plants
; Remote Sensing
; Switzerland
英文摘要: The ability to map vegetation and in particular individual trees is a key component in forest management and long-term forest monitoring. Here we present a novel approach for mapping individual tree species based on ensemble modeling, i.e. combining the projections of several modeling techniques in order to reduce uncertainty. Using statistical modeling in conjunction with high-resolution aerial imagery (50. cm spatial resolution) and topo-climatic variables (5. m spatial resolution), we map the distributions of six major tree species (3 broadleaf and 3 conifers) in a study area of North-Eastern Switzerland. We also compare the relative predictive power of both topo-climatic and remote-sensing variables for mapping the spatial tree patterns and assess the importance of calibration data quality on model performance. We evaluate our projections using cross-validation as well as with independent data. Overall, the evaluations that we obtain for our vegetation maps are in line with, or higher than, those in similar studies. Depending on the considered tree species, 47.8-85.6% of our samples were correctly predicted, and we obtain an overall CCR (correct classification rate) of 0.72 and a Cohen's kappa of 0.65. Comparing the predictive power of the different modeling techniques, we find that ensemble modeling (i.e. combining the projections of different individual modeling techniques) generally performs better than individual modeling techniques. © 2013 Elsevier B.V. Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/66239
Appears in Collections: 影响、适应和脆弱性
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作者单位: Swiss Federal Research Institute WSL, Zuercherstrasse 111, CH-8903 Birmensdorf, Switzerland
Recommended Citation:
Engler R.,Waser L.T.,Zimmermann N.E.,et al. Combining ensemble modeling and remote sensing for mapping individual tree species at high spatial resolution[J]. Forest Ecology and Management,2013-01-01,310