A Segmented Processing Approach of Eigenvector Spatial Filtering Regression for Normalized Difference Vegetation Index in Central China

Jiaxin Yang,Fan Yang,Meijie Chen,Ming Yao,Yumin Chen

doi:10.3390/ijgi7080330

Jiaxin Yang, Fan Yang + Show 3 more

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https://doi.org/10.3390/ijgi7080330

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Abstract

A segmented processing approach of eigenvector spatial filtering (ESF) regression is proposed to detect the relationship between NDVI and its environmental factors like DEM, precipitation, relative humidity, precipitation days, soil organic carbon, and soil base saturation in central China. An optimum size of 32 × 32 is selected through experiments as the basic unit for image segmentation to resolve the large datasets to smaller ones that can be performed in parallel and processed more efficiently. The eigenvectors from the spatial weights matrix (SWM) of each segmented image block are selected as synthetic proxy variables accounting for the spatial effects and aggregated to construct a global ESF regression model. Results show precipitation and humidity are more influential than other factors and spatial autocorrelation plays a vital role in vegetation cover in central China. Despite the increase in model complexity; the parallel ESF regression model performs best across all performance criteria compared to the ordinary least squared linear regression (OLS) and spatial autoregressive (SAR) models. The proposed parallel ESF approach overcomes the computational barrier for large data sets and is very promising in applying spatial regression modeling to a wide range of real world problem solving and forecasting.

Highlights

Vegetation cover is one of the most important indicators for ecological environment
A parallel eigenvector spatial filtering (ESF) regression approach for large size raster datasets of Normalized Difference Vegetation Index (NDVI) and its environmental factors based on segmentation is proposed to solve the problem of inaccuracy and uncertainty for regression model caused by spatial autocorrelation and insufficiency of calculating ability for large size raster datasets
A parallel ESF regression model based on raster segmentation is proposed for regression analysis of large, regular-squared raster datasets and applied to NDVI and its factors of DEM, PREC, relative humidity (RHU), DAYP, organic carbon (OC), and base saturation (BS)

Summary

Introduction

Vegetation cover is one of the most important indicators for ecological environment. It is important to study vegetation cover indicators and their relations with the influenced factors. An algorithm was reported to predict NDVI using precipitation data and air temperature data on the earth surface with a spatial resolution of 0.25◦ × 0.25◦ on the globe [13]. It has limited values for inference and prediction, as the model does not account for spatial autocorrelation; it works only in arid and semi-arid regions, and the resolution is too coarse for a broad application [13]

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Discussion

Conclusion