阿拉尔垦区近30年耕地变化及其驱动因子分析

doi:10.6046/gtzyyg.2020183

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耕地动态变化趋势及其驱动因子的明确,是确保国家粮食安全、水土资源合理开发利用和土地利用结构调整的重要依据。以南疆阿拉尔垦区为例,基于1990—2019年间7个重要时期的Landsat卫星遥感影像、人口、国内生产总值(gross domestic product,GDP)等数据,将波谱角 (spectral angle mapper, SAM)、神经网络 (artificial neural net, ANN)、最小距离(minimum distance classification, MDC)、最大似然 (maximum likelihood classification, MLC)和支持向量机 (support vector machine, SVM) 5种分类方法经全连接条件随机场(fully connected conditional random field, FC—CRF)处理后进行对比,从中筛选出最佳算法来解译遥感影像,利用解译结果分析耕地面积变化、类型转化和空间动态变化特征,并探讨耕地面积变化的主要驱动因子、作用路径和强度。结果表明,在5种分类算法中,SVM—CRF算法的分类精度最高,总体精度为0.95,Kappa系数为0.94,其他4类算法的总体精度为0.65~0.89,Kappa系数为0.58~0.86。研究区1990—2019年间耕地面积呈持续增长趋势,耕地面积净增长量为729.97 km²(312.21%),耕地转入和转出分别呈现出向外扩张和向内收缩趋势。总人口、GDP、全社会固定资产投资、农业生产总值和棉花价格是耕地面积变化的5个主要驱动因子,其中GDP对耕地面积变化的直接影响最大,棉花价格的直接影响最小,除GDP对耕地面积表现为负向作用外,其他4个因子对耕地面积变化均表现为正向作用。

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关键词 ：耕地变化, 驱动因子, 遥感, Landsat, 土地利用/覆被

Abstract：

The clarification of the dynamic change trend of cultivated land and its driving factors is an important basis for ensuring national food security, rationally developing and utilizing soil and water resources and adjusting land use structure. Taking Alar reclamation area in southern Xinjiang as an example and based on Landsat satellite remote sensing images, population, GDP and other data of seven important periods from 1990 to 2019, the authors selected the best algorithm to interpret remote sensing images by comparing the accuracy of five classification algorithms comprising SAM-CRF, ANN-CRF, MDC-CRF, MLC-CRF and SVM-CRF. Next, the characteristics of cultivated land area change, type transformation and spatial dynamic change were analyzed by using the interpretation results, and then the main driving factors, action path and intensity of cultivated land area change were discussed. The results show that the SVM-CRF algorithm has the highest classification accuracy among the five classification algorithms, with the overall accuracy of 0.95 and the Kappa coefficient of 0.94. The overall accuracy of the other four algorithms is between 0.65 and 0.89, and the Kappa coefficient is between 0.58 and 0.86. The area of cultivated land in the study area has continued to increase in the past three decades, and the net increase in cultivated land area is 729.97 km ² (312.21%). Cultivated land transfer-in and transfer-out has shown a trend of outward expansion and inward contraction, respectively. Total population, GDP, Total Investment in Fixed Assets, gross agricultural product and cotton price are the main driving factors for the change of cultivated land area,among which GDP has the greatest direct impact on the change of cultivated land area, while cotton price has the least impact. Except that GDP has a negative effect on cultivated land area, the other four factors have a positive effect on cultivated land area, and the overall performance of the five factors is a positive effect.

Key words： change of cultivated land driving factors remote sensing Landsat land use/cover

收稿日期: 2020-06-22 出版日期: 2021-07-21

ZTFLH:

TP79

基金资助:兵团中青年科技创新领军人才项目“棉田土壤剖面盐溃化的卫星遥感监测研究”(2020CB032);国家重点研发计划项目“土壤综合观测与智能服务平台研发与应用”(2018YFE0107000)

通讯作者: 彭杰

作者简介: 宋奇(1996-),男,硕士研究生,主要研究方向为国土资源与遥感。Email: tarimsongqi@163.com。

引用本文:

宋奇, 冯春晖, 高琪, 王明玥, 吴家林, 彭杰. 阿拉尔垦区近30年耕地变化及其驱动因子分析[J]. 国土资源遥感, 2021, 33(2): 202-212.
SONG Qi, FENG Chunhui, GAO Qi, WANG Mingyue, WU Jialin, PENG Jie. Change of cultivated land and its driving factors in Alar reclamation area in the past thirty years. Remote Sensing for Land & Resources, 2021, 33(2): 202-212.

链接本文:

https://www.gtzyyg.com/CN/10.6046/gtzyyg.2020183 或 https://www.gtzyyg.com/CN/Y2021/V33/I2/202

Fig.1 研究区地理位置图

Fig.2 1990—2019年阿拉尔垦区卫星遥感影像

Tab.1 阿拉尔垦区分类体系及解译标志

Fig.3 每类样本点个数和分布图

Tab.2 2019年SAM—CRF,ANN—CRF,MDC—CRF,MLC—CRF和SVM—CRF分类结果的精度对比

Fig.4 各方法分类结果比较

Fig.5 SVM和SVM—CRF算法结果局部区域对比

Tab.3 阿拉尔垦区用地类型精度评价^①

Fig.6 1990—2019年阿拉尔垦区土地利用/覆被图

Fig.7 1990—2019年阿拉尔垦区各用地类型所占比例

Fig.8 1990—2019年阿拉尔垦区各土地利用类型的年均变化率

Tab.4 1990—2019年阿拉尔垦区各地类变化转换矩阵

Fig.9 1990—2019年耕地空间动态变化图

Tab.5 阿拉尔垦区1990—2019年耕地面积变化与相关指标标准化后数值

Tab.6 耕地变化驱动力变量相关系数矩阵

Tab.7 驱动因子对耕地面积的通径系数^①

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