Dr. Bingwen Qiu Profile

Dr. Bingwen Qiu

at Fuzhou Univ

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Proceedings Article | 25 July 2007 Paper

Scale effect analysis of driving forces of arable land of Fujian province

Bingwen Qiu

Proceedings Volume 6753, 67531H (2007) https://doi.org/10.1117/12.761857

KEYWORDS: Soil science, Statistical analysis, Agriculture, Analytical research, Roads, Biological research, Spatial resolution, Climatology, Data mining, Geoinformatics

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Land use patterns are governed by a broad variety of potential driving forces and constraints which act over a large range of scales and multi-scale investigation of land use patterns is essential for full understanding of its complexity. The main purpose of this paper was to perform a multi-scale analysis of arable land distribution pattern of Fujian province by means of statistical analysis through overall study and each agro-zone respectively. 27 variables were selected as the candidate land use drivers representing bio-geophysical, socio-economic and infrastructural conditions. The basic spatial organization in the analysis was a 1km×1km geographical grid. Through aggregations of these cells, a total of 10 artificial aggregation levels were obtained. The independent models of the whole study area and each 6 agro-zones of arable land distribution patterns were constructed at multiple scales respectively. The results showed that Land use models varied with aggregation level and also between agro-zones. Independent variables explained more of the variance for the explanation of land use type at higher aggregation levels. Except slope, the highest ranking variable, other variables of the arable land use model vary between agro-zone I to VI. But the general rule is that arable land in all 6 agro-zones is strictly restricted by topographic factors which changes little along with time. It is argued that these types of analyses can support quantitative multi-scale understanding of land use, needed for the spatially explicit land use change models.

Proceedings Article | 28 October 2006 Paper

Modeling spatial distribution of land use taking into account spatial autocorrelation

Bingwen Qiu, Qinmin Wang

Proceedings Volume 6420, 64201A (2006) https://doi.org/10.1117/12.712987

KEYWORDS: Autoregressive models, Data modeling, Soil science, Statistical analysis, Roads, Landsat, Stereolithography, Statistical methods, Climatology, Earth observing sensors

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Land use drivers that best describe land use patterns quantitatively are often selected through regression analysis. A problem using conventional statistical methods in spatial land use analysis is that these methods assume the data to be statistically independent while spatial land use data have the tendency to be dependent, known as spatial autocorrelation. Two different scales of study area, Fujian Province and Longhai county are selected. In this paper, Moran's I is used to describe spatial autocorrelation of dependent and independent variables and spatial autoregressive models which incorporate both regression and spatial autocorrelation are constructed. 5 main land use types in Fujian Province, 9 main land use types in Longhai county and all candidate land use driving factors show positive spatial autocorrelation. The occurrence of spatial autocorrelation is highly dependent on the aggregation level. Results also show that spatial autoregressive models yield residuals without spatial autocorrelation and have a better goodness-of-fit. The spatial autoregressive model is statistically sound in the presence of spatially dependent data in contrast with the standard linear model.

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