DOI: 10.1016/j.jag.2014.05.004
Scopus记录号: 2-s2.0-84904060559
论文题名: Estimation of floodplain aboveground biomass using multispectralremote sensing and nonparametric modeling
作者: Güneralp I ; , Filippi A ; M ; , Randall J
刊名: International Journal of Applied Earth Observation and Geoinformation
ISSN: 15698432
出版年: 2014
卷: 33, 期: 1 起始页码: 119
结束页码: 126
语种: 英语
英文关键词: Aboveground biomass
; Floodplain
; Multivariate adaptive regression splines
; Remote sensing
; River meander
; Stochastic gradient boosting
Scopus关键词: aboveground biomass
; floodplain forest
; mapping
; meander
; multivariate analysis
; remote sensing
; river
英文摘要: Floodplain forests serve a critical function in the global carbon cycle because floodplains constitute an important carbon sink compared with other terrestrial ecosystems. Forests on dynamic floodplain land-capes, such as those created by river meandering processes, are characterized by uneven-aged trees and exhibit high spatial variability, reflecting the influence of interacting fluvial, hydrological, and ecological processes. Detailed and accurate mapping of aboveground biomass (AGB) on floodplain landscapes char-acterized by uneven-aged forests is critical for improving estimates of floodplain-forest carbon pools, which is useful for greenhouse gas (GHG) life cycle assessment. It would also help improve our process understanding of biomorphodynamics of river-floodplain systems, as well as planning and monitoring of conservation, restoration, and management of riverine ecosystems. Using stochastic gradient boosting(SGB), multivariate adaptive regression splines (MARS), and Cubist, we remotely estimate AGB of a bot-tomland hardwood forest on a meander bend of a dynamic lowland river. As predictors, we use 30-m and10-m multi spectral image bands (Landsat 7 ETM+ and SPOT 5, respectively) and ancillary data. Our find-ings show that SGB and MARS significantly outperform Cubist, which is used for U.S. national-scale forest biomass mapping. Across all data-experiments and algorithms, at 10-m spatial resolution, SGB yields the best estimates (RMSE = 22.49 tonnes/ha; coefficient of determination (R2) = 0.96) when geomorphomet-ric data are also included. On the other hand, at 30-m spatial resolution, MARS yields the best estimates(RMSE = 29.2 tonnes/ha; R2= 0.94) when image-derived data are also included. By enabling more accurate AGB mapping of floodplains characterized by uneven-aged forests, SGB and MARS provide an avenue for improving operational estimates of AGB and carbon at local, regional/continental, and global scales. © 2014 Elsevier B.V. Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/79754
Appears in Collections: 气候变化事实与影响
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作者单位: Department of Geography, Texas A and M University, 810 Eller O and M Building, 3147 TAMU, College Station, TX 77843-3147, United States
Recommended Citation:
Güneralp I,, Filippi A,M,et al. Estimation of floodplain aboveground biomass using multispectralremote sensing and nonparametric modeling[J]. International Journal of Applied Earth Observation and Geoinformation,2014-01-01,33(1)