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遥感技术与应用  2022, Vol. 37 Issue (3): 550-563    DOI: 10.11873/j.issn.1004-0323.2022.3.0550
农业遥感专栏     
基于时序遥感数据的福州市耕地非农化特征及驱动因子分析
丁书培1(),李蒙蒙1(),汪小钦1,李琳1,吴瑞姣2,黄姮2
1.福州大学空间数据挖掘与信息共享教育部重点实验室、卫星空间信息技术综合应用国家地方联合工程研究中心、数字中国研究院(福建),福建 福州 350108
2.福建省地质测绘院,福建 福州 350108
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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摘要:

耕地是粮食生产的基本载体,及时准确地获取耕地非农化信息,对于耕地资源管理和政策实施具有重要意义。为探究福州市近30 a耕地非农化变化规律,基于谷歌地球引擎(Google Earth Engine,GEE)和随机森林方法,利用多时相Landsat遥感影像提取了福州市1989、2000、2010和2019年耕地空间分布信息,并在此基础上利用土地转移矩阵、网格单元法和地理探测器等方法,分析了福州市耕地非农化的重要特征及其驱动因子。结果表明:①基于GEE平台的随机森林方法可有效提取南方多云多雨地区的耕地信息,土地利用分类总体精度高于90%,Kappa系数大于0.85;②福州市耕地空间分布不均匀,呈现东多西少,耕地面积随时间推移不断减少,耕地非农化呈现“快—慢—平”的特征。耕地非农化主要发生在高程100 m和坡度10°以下区域,耕地非农化类型主要为园林地和建设用地,其中西部地区主要为园林地,中东部地区为建设用地;③耕地非农化是由自然和社会因素共同驱动的结果,自然因素是耕地非农化的先决条件,城镇化增长率与人口数量增长率是导致耕地非农化主要驱动因素,其中城镇化增长率和第一产业比重增长率是耕地非农化“快—慢—平”的关键因素。

关键词: 耕地非农化多时相遥感随机森林GEE地理探测器    
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
收稿日期: 2021-08-13 出版日期: 2022-08-25
ZTFLH:  S127  
基金资助: 国家重点研发计划项目(2017YFB0504203);中央引导地方发展专项(2017L3012)
通讯作者: 李蒙蒙     E-mail: N195520005@fzu.edu.cn;mli@fzu.edu.cn
作者简介: 丁书培(1996-),男,安徽阜阳人,硕士研究生,主要从事农业遥感方面的研究。E?mail: N195520005@fzu.edu.cn
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引用本文:

丁书培,李蒙蒙,汪小钦,李琳,吴瑞姣,黄姮. 基于时序遥感数据的福州市耕地非农化特征及驱动因子分析[J]. 遥感技术与应用, 2022, 37(3): 550-563.

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.

链接本文:

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

图1  研究区示意图
因素类型指标指标描述数据来源
社会因子人口数量增长率/%反映地区人口规模变化,人口增长会引起土地资源压力

《福州年鉴》

《福州统计年鉴》

城镇化增长率/%反映城市化水平的重要指标
第一产业比重增长率/%反映农业在社会经济发展中的重要性
GDP增长率/%反映社会经济发展的重要指标
到公路距离/m公路是人类活动地域联系重要载体国家基础地理信息中心
到铁路距离/m铁路是人类活动地域联系重要载体
到城镇距离/m城镇驻点反映人类活动的地点
自然因子到水系距离/m水源对于农作物灌溉有着重要的作用
高程/m耕地主要分布在海拔相对较为平坦区域GEE平台
坡度/°坡度大小影响农业生产中机械化水平
坡向/°坡向的不同对于农作物生长产生一定的影响
土壤类型不同土壤类型理化性质,影响土地利用变化中国科学院资源环境数据云平台
表1  耕地非农化驱动因子指标
图2  福州市耕地变化分析方法流程图
图3  1989~2019年福州市土地利用空间分布图
类型1989年2000年2010年2019年

UA

/%

PA

/%

UA/%

PA

/%

UA

/%

PA

/%

UA

/%

PA

/%

耕地89.7087.1489.1890.4193.3394.2285.5793.87
园林地93.4295.9496.8710098.4695.529894.23
水体96.4210010010097.6696.59100100
建设用地97.9110089.7483.5382.3595.4585.4585.45
未利用地87.580.767572.729269.7083.3371.42
整体精度/%93.0390.3591.4790.94
Kappa系数0.910.870.890.87
表2  土地利用分类精度
图4  分类结果对比图
图5  1989~2019年福州市耕地非农化转移类型
图6  1989~2000年福州市耕地非农化空间分布
图7  1989~2019年耕地非农化与高程和坡度关系
图8  1989~2019年福州市耕地非农化率网格图
驱动因子1989~2000年2000~2010年2010~2019年1989~2019年
Q值重要性Q值重要性Q值重要性Q值重要性
人口数量增长率0.059 990.068 5100.249 130.231 04
城镇化增长率0.200 320.195 620.132 080.082 69
第一产业比重增长率0.123 730.120 170.115 3100.085 18
GDP增长率0.059 5100.172 140.161 750.082 210
到公路距离0.115 450.117 580.122 990.165 05
到铁路距离0.020 4110.041 2110.026 7110.031 311
到城镇距离0.079 270.123 060.141 960.142 36
到水系距离0.076 180.106 290.136 370.121 87
土壤类型0.122 940.151 250.192 340.253 42
高程0.208 610.293 910.419 610.392 31
坡度0.105 460.177 130.273 720.233 33
坡向0.001 9120.001 3120.005 4120.003 812
表3  社会和自然因子对耕地非农化的影响程度
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