DOI: 10.1016/j.jag.2016.08.007
Scopus记录号: 2-s2.0-85003845900
论文题名: Forest aboveground biomass estimation in Zhejiang Province using the integration of Landsat TM and ALOS PALSAR data
作者: Zhao P ; , Lu D ; , Wang G ; , Liu L ; , Li D ; , Zhu J ; , Yu S
刊名: International Journal of Applied Earth Observation and Geoinformation
ISSN: 15698432
出版年: 2016
卷: 53 起始页码: 1
结束页码: 15
语种: 英语
英文关键词: Aboveground biomass
; ALOS PALSAR
; Data combination and fusion
; Data saturation
; Landsat TM
; Stratification
Scopus关键词: aboveground biomass
; accuracy assessment
; algorithm
; ALOS
; forest ecosystem
; Landsat thematic mapper
; PALSAR
; remote sensing
; satellite data
; stratification
; China
; Zhejiang
英文摘要: In remote sensing–based forest aboveground biomass (AGB) estimation research, data saturation in Landsat and radar data is well known, but how to reduce this problem for improving AGB estimation has not been fully examined. Different vegetation types have their own species composition and stand structure, thus they have different data saturation values in Landsat or radar data. Optical and radar data also have different characteristics in representing forest stand structures, thus effective use of their features may improve AGB estimation. This research examines the effects of Landsat Thematic Mapper (TM) and ALOS PALSAR L-band data and their integrations in forest AGB estimation of Zhejiang Province, China, and the roles of textural images from both datasets. The linear regression models of AGB were conducted by using (1) Landsat TM alone, (2) ALOS PALSAR data alone, (3) their combination as extra bands, and (4) their data fusion, based on non-stratification and stratification of vegetation types, respectively. The results show that (1) overall, Landsat TM data perform better than PALSAR data, but the latter can produce more accurate estimates for bamboo and shrub, and for forests with AGB values less than 60 Mg/ha; (2) the combination of TM and PALSAR data as extra bands can greatly improve AGB estimation performance, but their fusion using the modified high-pass filter resolution-merging technique cannot; (3) textures are indeed valuable in AGB estimation, especially for forests with complex stand structures such as mixed forests and pine forests with understories of broadleaf species; (4) stratification of vegetation types can improve AGB estimation performance; and (5) the results from the linear regression models are characterized by overestimation and underestimation for the smaller and larger AGB values, respectively, and thus, selecting non-linear models or non-parametric algorithms may be needed in future research. © 2016 Elsevier B.V. Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/80133
Appears in Collections: 气候变化事实与影响
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作者单位: Key Laboratory of Carbon Cycling in Forest Ecosystems and Carbon Sequestration of Zhejiang Province, School of Environmental & Resource Sciences, Zhejiang Agriculture and Forestry University, Lin An, Zhejiang Province, China; Center for Global Change and Earth Observations, Michigan State University, East Lansing, MI, United States; Department of Geography, Southern Illinois University at CarbondaleIL, United States; Zhejiang Forestry Academy, Hangzhou, Zhejiang Province, China; School of Forestry and Biotechnology, Zhejiang Agriculture and Forestry University, Lin An, Zhejiang Province, China
Recommended Citation:
Zhao P,, Lu D,, Wang G,et al. Forest aboveground biomass estimation in Zhejiang Province using the integration of Landsat TM and ALOS PALSAR data[J]. International Journal of Applied Earth Observation and Geoinformation,2016-01-01,53