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  

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

作　　者：丁书培 李蒙蒙 汪小钦[1] 李琳[1] 吴瑞姣 黄姮 Ding Shupei;Li Mengmeng;Wang Xiaoqin;Li Lin;Wu Ruijiao;Huang Heng(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;Fujian Geologic Surveying and Mapping Institute,Fuzhou 350108,China)

机构地区：[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

出　　处：《遥感技术与应用》2022年第3期550-563,共14页Remote Sensing Technology and Application

基　　金：国家重点研发计划项目(2017YFB0504203);中央引导地方发展专项(2017L3012)。

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。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 2019based 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.

关 键 词：耕地非农化 多时相遥感 随机森林 GEE 地理探测器 

分 类 号：S127[农业科学—农业基础科学] TP75[自动化与计算机技术—检测技术与自动化装置]