Analysis of Change in Maize Plantation Distribution and Its Driving Factors in Heilongjiang Province, China
Analysis of Change in Maize Plantation Distribution and Its Driving Factors in Heilongjiang Province, China
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DOI：
10.3390/rs14153590
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发表时间：
2022-07
期刊：
影响因子：
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通讯作者：
Ruizhi Guo;Xiufang Zhu;Ce Zhang;Changxiu Cheng
中科院分区：
文献类型：
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作者：
Ruizhi Guo;Xiufang Zhu;Ce Zhang;Changxiu Cheng
准确识别玉米种植分布并及时检查关键时空驱动因素是支持农业生产估算和发展决策的一种做法。以往的研究很少使用高效的云处理方法提取作物分布，在驱动力分析中往往独立考虑气象和社会经济因素。基于谷歌Earth Engine （GEE）平台，采用分类回归树（CART）和随机森林（RF）算法提取玉米的空间分布特征。结合遥感、气象和统计资料，采用趋势分析、核密度估计和标准差椭圆分析等方法分析了县域尺度下玉米种植比例的时空变化特征，采用偏相关分析和地理探测器等方法探讨了玉米种植比例时空变化的驱动力。在中国黑龙江省的实证结果表明：(1)CART算法比RF算法具有更高的分类精度；②75%以上的县域，特别是高纬度地区，MPP呈上升趋势；(3)影响MPP年际波动的主要气候因子是相对湿度；(4)社会经济因素对MPP空间分布的影响显著大于气象因素，气温是最重要的气象因素，农户数量是影响MPP空间分布最重要的社会经济因素。不同因素间的交互作用大于单一因素；(5)气象因子与MPP的相关性在不同的纬度区域和地形上存在差异。该研究为作物种植布局优化调整、农业发展规划和政策制定提供了重要参考。
Accurate identification of maize plantation distribution and timely examination of key spatial-temporal drivers is a practice that can support agricultural production estimates and development decisions. Previous studies have rarely used efficient cloud processing methods to extract crop distribution, and meteorological and socioeconomic factors were often considered independently in driving force analysis. In this paper, we extract the spatial distribution of maize using classification and regression tree (CART) and random forest (RF) algorithms based on the Google Earth Engine (GEE) platform. Combining remote sensing, meteorological and statistical data, the spatio-temporal variation characteristics of maize plantation proportion (MPP) at the county scale were analyzed using trend analysis, kernel density estimation, and standard deviation ellipse analysis, and the driving forces of MPP spatio-temporal variation were explored using partial correlation analysis and geodetectors. Our empirical results in Heilongjiang province, China showed that (1) the CART algorithm achieved higher classification accuracy than the RF algorithm; (2) MPP showed an upward trend in more than 75% of counties, especially in high-latitude regions; (3) the main climatic factor affecting the inter-annual fluctuation of MPP was relative humidity; (4) the impact of socioeconomic factors on MPP spatial distribution was significantly larger than meteorological factors, the temperature was the most important meteorological factor, and the number of rural households was the most important socioeconomic factor affecting MPP spatial distribution. The interaction between different factors was greater than a single factor alone; (5) the correlation between meteorological factors and MPP differed across different latitudinal regions and landforms. This research provides a key reference for the optimal adjustment of crop cultivation distribution and agricultural development planning and policy.