The Use of Time Series Remote Sensing Data to Analyze the Characteristics of Non-agriculture Farmland and Their Driving Factors in Fuzhou

doi:10.11873/j.issn.1004-0323.2022.3.0550

Remote Sensing Technology and Application

2022, Vol. 37

Issue (3): 550-563 DOI: 10.11873/j.issn.1004-0323.2022.3.0550

The Use of Time Series Remote Sensing Data to Analyze the Characteristics of Non-agriculture Farmland and Their Driving Factors in Fuzhou

Shupei Ding¹(

),Mengmeng Li¹(

),Xiaoqin Wang¹,Lin Li¹,Ruijiao Wu²,Heng Huang²

^1.Key Laboratory of Spatial Data Mining and Information Sharing of Ministry of Education，National & Local Joint Engineering Research Center of Satellite Geospatial Information Technology，Fuzhou University，The Academy of Digital China （Fujian），Fuzhou University，Fuzhou 350108，China
^2.Fujian Geologic Surveying and Mapping Institute，Fuzhou 350108，China

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Abstract

Farmland is important for food production. It is thus of great importance to obtain timely and accurate information regarding non-agricultural farmlands for land resource management and policymaking. To investigate the changes of non-agricultural farmlands in Fuzhou over past 30 years， this study extracted the spatial information of farmlands using multi-temporal Landsat remote sensing images in 1989， 2000， 2010 and 2019 based on the Google Earth Engine （GEE） and random forest methods. We then used land transfer matrix， grid element method and geographic detector techniques to analyze the characteristics and driving factors of non-agricultural farmlands changes. The results show that：（1） The GEE platform integrating with random forest is suitable to extract farmlands in cloudy and rainy areas in southern part of China. The overall accuracy of the extracted farmlands is higher than 90%， and the Kappa coefficient is greater than 0.85. （2） The farmlands in Fuzhou has an imbalanced spatial distribution， where the area of farmlands deceases from east to west along time. From 1989 to 2019， the farmland changes mainly occurred at areas with an elevation of 100 m and a slope of less than 10°. The changed farmlands mainly consisted of forestlands and construction lands， in which the western region was mainly forestland， and the central and eastern region was construction land. （3） The natural factors are the prerequisite for the conversion of cultivated land， and the growth rate of urbanization and population data are the main driving factors. Moreover， urbanization rate and the proportion of primary industry growth rate were the factors forming the “fast-slow-stable” pattern of farmland non-agriculturalization.

Key words: Non-agricultural farmlands Multi-temporal remote sensing Random forest GEE Geographic detector

Received: 13 August 2021 Published: 25 August 2022

ZTFLH:

S127

Corresponding Authors: Mengmeng Li E-mail: N195520005@fzu.edu.cn;mli@fzu.edu.cn

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	Shupei Ding
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	Heng Huang

Cite this article:

Shupei Ding,Mengmeng Li,Xiaoqin Wang,Lin Li,Ruijiao Wu,Heng Huang. The Use of Time Series Remote Sensing Data to Analyze the Characteristics of Non-agriculture Farmland and Their Driving Factors in Fuzhou. Remote Sensing Technology and Application, 2022, 37(3): 550-563.

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http://www.rsta.ac.cn/EN/10.11873/j.issn.1004-0323.2022.3.0550 OR http://www.rsta.ac.cn/EN/Y2022/V37/I3/550

Fig.1 The intensity pattern among the central plains economic zone

Table 1 Indicators of driving factors of farmland conversion

Fig.2 Flowchart of non-agriculture farmland analysis method in Fuzhou

Fig.3 Spatial distribution of land use in Fuzhou from 1989 to 2019

Table 2 Land use classification accuracy

Fig.4 Comparison of classification results

Fig.5 Non-agricultural transfer types of farmland in Fuzhou from 1989 to 2019

Fig.6 Spatial distribution of non-agriculture farmland in Fuzhou from 1989 to 2000

Fig. 7 Relationship between non-agriculture farmland and elevation and slope during 1989~2019

Fig.8 Grid diagram of farmland conversion in Fuzhou from 1989 to 2019

Table 3 Influence degree of social and natural factors on farmland conversion

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