| 基于多时相Sentinel-1A的丘陵山区耕地非粮化特征识别及驱动机制 |
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| 引用本文： | 徐国良,卢凌莹,杨璨,黄李澄,万春燕.基于多时相Sentinel-1A的丘陵山区耕地非粮化特征识别及驱动机制[J].农业工程学报,2023,39(22):236-245. |
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| 作者姓名： | 徐国良 卢凌莹 杨璨 黄李澄 万春燕 |
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| 作者单位： | 江西财经大学财税与公共管理学院, 南昌 330013;华中农业大学公共管理学院, 武汉 430070;中建四局芜湖建设投资有限公司, 芜湖 241000;江西财经大学信息管理学院, 南昌 330032 |
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| 基金项目： | 国家自然科学基金项目(42161047) |
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| 摘 要： | 为更加科学地识别丘陵山区耕地非粮化水平特征，探究耕地非粮化驱动机制，该研究采用多时相Sentinel-1A后向散色系数分析、监督分类、空间特征分析、地理探测器等方法，选取江西省典型丘陵山区修水县开展实证研究，探究了丘陵山区耕地非粮化的技术识别、空间特征及影响机制。结果表明：1）修水县耕地非粮化面积17899.59 hm2，耕地非粮化率为57.47%，并呈现东南及西北集聚的特征，且呈现空间正相关性，包括了“低-低”、“高-高”、“低-高”集聚等不同形式。2）在社会经济特征上，修水县耕地非粮化主要呈环工业中心镇分布的特征，分布密度西高东低，逐渐向东北倾斜，且经济作物主导的乡镇非粮化水平显著高于其他地区。3）修水县耕地非粮化受多个因子综合影响，包括到农村道路的距离、坡度、粮食生产功能区、高程、水土流失强度、到城镇中心的距离、到城市公路的距离、到水源的距离。各因子对耕地非粮化的影响呈非线性增强的相互作用关系，其中尤以到农村道路的距离、到城镇中心的距离、粮食功能主产区等3个因素与其他因子的相互作用特征明显。该研究成果可为新形势下推进耕地资源保护利用提供参考，为科学诊断区域耕地非粮化问题提供依据。
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| 关 键 词： | 土地利用 耕地 丘陵山区 非粮化Sentinel-1A |
| 收稿时间： | 2023/5/5 0:00:00 |
| 修稿时间： | 2023/10/30 0:00:00 |
Identification and driving mechanisms of non-grain cultivated land in hilly and mountainous areas based on multi-temporal Sentinel-1A images |
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| XU Guoliang,LU Lingying,YANG Can,HUANG Licheng,WAN Chunyan.Identification and driving mechanisms of non-grain cultivated land in hilly and mountainous areas based on multi-temporal Sentinel-1A images[J].Transactions of the Chinese Society of Agricultural Engineering,2023,39(22):236-245. |
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| Authors: | XU Guoliang LU Lingying YANG Can HUANG Licheng WAN Chunyan |
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| Institution: | School of Public Finance & Public Administration, Jiangxi University of Finance and Economics, Nanchang 330013, China;College of Public Administration, Huazhong Agricultural University, Wuhan 430070, China;China Construction Fourth Bureau Wuhu Construction Investment Company Limited, Wuhu 241000, China; School of Information Management, Jiangxi University of Finance and Economics, Nanchang 330032, China |
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| Abstract: | This study aims to better define the features of non-grain cultivated land in hilly and mountainous areas, in order to clarify the driving mechanisms and causes of its occurrence. An empirical study was carried out in Xiushui County in Jiangxi Province, China. The specific procedures were as follows. Firstly, the rice planting pattern was obtained using the multi-temporal Sentinel-1A backward dispersion coefficient analysis in conjunction with field research, and high-resolution Google Earth images for classification and supervision. Secondly, this non-grain cultivated land was generated to remove the rice planting patterns from permanent basic agriculture, after which the permanent basic farmland pattern was superimposed. The geographical distribution of non-grain cultivated land in the case sites was analyzed using the standard deviation ellipse, kernel density analysis, and spatial autocorrelation. Lastly, the geographic detector analysis was conducted to evaluate the impact of various factors in three dimensions of cultivated land site, traffic location, and policy control factors on non-grain cultivated land. The interaction relationship between the factors was evaluated as well. The results demonstrated that: 1) The area and percentage of non-grain cultivated land were 17899.59 hm2 and 57.47%, respectively, in the study area. The geographical distribution was found in the two distinct groups in the southeast and northwest. The spatial distribution patterns also shared with the low-low, high-high, and low-high agglomeration with a substantial positive correlation. 2) The majority of non-grain cultivated land was also characterized by the distribution patterns in the vicinity of the industrial center towns. The distribution density was the highest in the west, while the lowest in the east, with a progressive decline towards the northeast. Furthermore, the cash crop cultivation resulted in a much greater amount of non-grain usage, compared with the locations. 3) Some variables shared an impact on the non-grain cultivated land, including the distance to rural roads, slope, elevation, decision-making on the main grain-producing area, soil erosion intensity, and the distance to town centers, urban highways, and water resources. The types of driver interactions were all nonlinear enhancement. Particularly, three driving factors interacted strongly with the rest: distance to town centers, distance to rural highways, and decision-making on the main grain-producing area. The measurement index of non-grain cultivated land was then investigated using multi-source data analysis and multi-dimensional influencing factors, in terms of the definition of non-grain crops and long-term time series images. More comparable measurement was required to determine the hot spots at the regional level of non-grain farmland. The findings can provide a sound foundation for the development of differentiated governance and control measures. The protection and use of cultivated land resources can be better realized for the scientific diagnosis of regional farmland non-grain production. |
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| Keywords: | land use cultivated land hilly and mountainous area non-grain cultivated land Sentinel-1A |
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