Developing Non-Negative Spatial Autoregressive Models for Better Exploring Relation Between Nighttime Light Images and Land Use Types

Honghan Zheng,Huayi Wu,Zhipeng Gui,Aihong Song

doi:10.3390/rs12050798

Abstract

Exploring the relationship between nighttime light and land use is of great significance to understanding human nighttime activities and studying socioeconomic phenomena. Models have been studied to explain the relationships, but the existing studies seldom consider the spatial autocorrelation of night light data, which leads to large regression residuals and an inaccurate regression correlation between night light and land use. In this paper, two non-negative spatial autoregressive models are proposed for the spatial lag model and spatial error model, respectively, which use a spatial adjacency matrix to calculate the spatial autocorrelation effect of light in adjacent pixels on the central pixel. The application scenarios of the two models were analyzed, and the contribution of various land use types to nighttime light in different study areas are further discussed. Experiments in Berlin, Massachusetts and Shenzhen showed that the proposed methods have better correlations with the reference data compared with the non-negative least-squares method, better reflecting the luminous situation of different land use types at night. Furthermore, the proposed model and the obtained relationship between nighttime light and land use types can be utilized for other applications of nighttime light images in the population, GDP and carbon emissions for better exploring the relationship between nighttime remote sensing brightness and socioeconomic activities.

Highlights

Nighttime light recorded by satellites represents what human beings use at night for production and living, which is an effective means to study the spatial distribution of human nocturnal activities [1,2]
From the nighttime light intensity (NLI) of different land use types, we found that the major sources of nighttime light in the three areas varied, as illustrated in Table A5 in the Appendix A
By observing NLIs of the same land use types in different cities, we found that forests and shrubs had relatively small NLIs in each city, while impervious RsuemrfoatecSeesnsa.n20d20b,a1r2e, 7l9a8nd had large NLIs

Summary

Introduction

Nighttime light recorded by satellites represents what human beings use at night for production and living, which is an effective means to study the spatial distribution of human nocturnal activities [1,2]. It has been confirmed that there is a strong linear correlation between night light brightness values and various economic indicators in a region [2,3,4]. Night light data have been widely used to simulate the spatial distribution of the population, GDP, carbon emissions and other fields [4,5,6]. Studies have shown that the correlation between night light and economic indicators varies from region to region [7,8,9]. Only by understanding the causes and distribution pattern of night light can we more accurately explain and analyze the distribution rules of human nighttime activities [13,14], study socioeconomic problems [15,16] and prevent and control light pollution [17,18] with night light data

Objectives

Methods

Results

Discussion

Conclusion