Spatial variability in scale transferring of vegetation LAI inversed from remotely sensed data

Jian Chen; Jingjing Li; Shanyou Zhu; Shaoxiang Ni

doi:10.1117/12.774797

14 November 2007 Spatial variability in scale transferring of vegetation LAI inversed from remotely sensed data

Jian Chen, Jingjing Li, Shanyou Zhu, Shaoxiang Ni

Proceedings Volume 6790, MIPPR 2007: Remote Sensing and GIS Data Processing and Applications; and Innovative Multispectral Technology and Applications; 67904M (2007) https://doi.org/10.1117/12.774797
Event: International Symposium on Multispectral Image Processing and Pattern Recognition, 2007, Wuhan, China

Abstract

In inversing Leaf Area Index (LAI) from remotely sensed data, the transformation of the remotely sensed data from one kind of resolution to another has become a significant problem because of the heterogeneity in pixel. In this paper, based on an analysis of the reasons for error appearing in LAI inversion, the spatial heterogeneity in pixel was described by semivariance. The following conclusions were obtained from this study: In the study area, the spatial heterogeneity of reeds is caused by both the random element and the extent of spatial self-correlation. These factors can be described by the parameters of semivariogram, i.e., nugget and sill. In a pure pixel, little variation was found between the 30m and 60m scale, which means that the scaling problem for pure pixels may be ignored. However, while the degree of heterogeneity within a pixel increases, the spatial heterogeneity in the pixel with larger scale may be somewhat hided compared with the pixel with smaller scale. Results also showed that valid spatial self-correlation scale of reeds in the study area is within 360m.

Citation Download Citation

Jian Chen, Jingjing Li, Shanyou Zhu, and Shaoxiang Ni "Spatial variability in scale transferring of vegetation LAI inversed from remotely sensed data", Proc. SPIE 6790, MIPPR 2007: Remote Sensing and GIS Data Processing and Applications; and Innovative Multispectral Technology and Applications, 67904M (14 November 2007); https://doi.org/10.1117/12.774797

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KEYWORDS

Remote sensing

Vegetation

MODIS

Atmospheric corrections

Error analysis

Spatial resolution

Statistical analysis

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