DOI: 10.1016/j.jag.2015.05.004
Scopus记录号: 2-s2.0-84937885945
论文题名: Mapping forest leaf area index using reflectance and textural information derived from WorldView-2 imagery in a mixed natural forest area in Florida, US
作者: Pu R ; , Cheng J
刊名: International Journal of Applied Earth Observation and Geoinformation
ISSN: 15698432
出版年: 2015
卷: 42 起始页码: 11
结束页码: 23
语种: 英语
英文关键词: Canonical correlation analysis
; Landsat TM
; Leaf area index (LAI)
; Texture measure
; Vegetation index
; WorldView-2
Scopus关键词: canonical analysis
; forest canopy
; Landsat thematic mapper
; leaf area index
; remote sensing
; satellite imagery
; spectral reflectance
; WorldView
; Florida [United States]
; United States
英文摘要: The leaf area index (LAI) of plant canopies is an important structural parameter that controls energy, water, and gas exchanges of plant ecosystems. Remote sensing techniques may offer an alternative for measuring and mapping forest LAI at a landscape scale. Given the characteristics of high spatial/spectral resolution of the WorldView-2 (WV2) sensor, it is of significance that the textural information extracted from WV2 multispectral (MS) bands will be first time used in estimating and mapping forest LAI. In this study, LAI mapping accuracies would be compared from (a) spatial resolutions between 2-m WV2 MS data and 30-m Landsat TM imagery, (b) the nature of variables between spectrum-based features and texture-based features, and (c) sensors between TM and WV2. Therefore spectral/textural features (SFs) were first selected and tested; then a canonical correlation analysis was performed with different data sets of SFs and LAI measurement; and finally linear regression models were used to predict and map forest LAI with canonical variables calculated from image data. The experimental results demonstrate that for estimating and mapping forest LAI, (i) using high resolution data (WV2) is better than using relatively low resolution data (TM); (ii) extracted from the same WV2 data, texture-based features have higher capability than that of spectrum-based features; (iii) a combination of spectrum-based features with texture-based features could lead to even higher accuracy of mapping forest LAI than their either one separately; and (iv) WV2 sensor outperforms TM sensor significantly. However, we need to address the possible overfitting phenomenon that might be brought in by using more input variables to develop models. In addition, the experimental results also indicate that the red-edge band in WV2 was the worst on estimating LAI among WV2 MS bands and the WV2 MS bands in the visible range had a much higher correlation with ground measured LAI than that red-edge and NIR bands did. © 2015 Elsevier B.V. Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/79475
Appears in Collections: 气候变化事实与影响
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作者单位: School of Geosciences, University of South Florida, 4202 E. Fowler Avenue, NES 107, Tampa, FL, United States
Recommended Citation:
Pu R,, Cheng J. Mapping forest leaf area index using reflectance and textural information derived from WorldView-2 imagery in a mixed natural forest area in Florida, US[J]. International Journal of Applied Earth Observation and Geoinformation,2015-01-01,42