Abstract
Current remote sensing scaling research highlights the ability to quantitatively represent spatial heterogeneity which has become the core issue of many studies. However, the influence of the difference in the land cover types of subpixels is seldom considered. Therefore, in the course of quantitatively studying the spatial heterogeneity induced by the leaf area index (LAI) scaling transfer process, this paper uses the contexture of the surface parameter to express the subpixel differences of each pixel and constructs a spatial heterogeneity coefficient ( C <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">sh</sub> ) by means of information entropy, class variance and class contribution degree. Numerical simulation and analysis of the spatial heterogeneity coefficient changes with different combinations of 3 land cover types (water, building/soil, and vegetation) are carried out, and the trends are summarized. On this basis, with the help of measured data in the study area, a pixel size of 30 m is defined; then, pixels of size 8×8 and 16×16 are upscaled to test the correlation between the scaling error ( e) and C <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">sh</sub> . The validity of C <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">sh</sub> is verified. The results show that both parameters achieve a high linear positive correlation at different scales, and the determination coefficient reaches 0.923 and 0.984, respectively. A regression relationship is then established between them to correct the scaling error, and as a result, an excellent correction is achieved. The RMSE decreases from 0.3329 before correction to 0.1457 after correction of e and from 0.2887 before correction to 0.0766 after correction of C <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">sh</sub> . This research has a certain reference importance for remote sensing scaling transfer studies and quantitative representations of spatial heterogeneity.
Highlights
In studying physical mechanisms, the scaling effect of quantitative remote sensing originates from the spatial complexity of land cover, which is reflected in the spatial structure, radiation character, difference in vegetation composition at the canopy level, scale difference at the remote sensing data pixel level and difference at the subpixel level [1]–[3] (Li and Wang, 1998; Li xiaowen et al, 1999, 2013)
When two land cover types are mixed, i.e., water body-vegetation mixture, water body-building/soil mixture, building/soil-vegetation mixture, the results show that the Csh curve is normally distributed, with a peak value at a 0.5 ratio, and the spatial heterogeneity of water body vegetation is higher, which is verified by a similar study in which Zhangyan Jiang (2006) found that there is a large bias in NDVI values at different resolutions in landscapes containing vegetation and open water [31]
This paper discusses the quantitative representation of spatial heterogeneity, one of the important factors driving the scaling transfer of leaf area index (LAI), constructs an expression formula, and numerically simulates the spatial heterogeneity coefficient of water bodies, buildings/soils and vegetation types with different mixing modes
Summary
The scaling effect of quantitative remote sensing originates from the spatial complexity of land cover, which is reflected in the spatial structure, radiation character, difference in vegetation composition at the canopy level, scale difference at the remote sensing data pixel level and difference at the subpixel level [1]–[3] (Li and Wang, 1998; Li xiaowen et al, 1999, 2013). Based on the three underlying surfaces of forests, farmland, and cities, Woodcock & Strahler (1988) utilized the local variance method to study the relationship between remote sensing classification accuracy and scale and found that the local variance of an image changes with its spatial scale [11].
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