Sentinel-1-based spatial differentiation study of the planting structures in Karst plateau mountainous areas
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摘要: 喀斯特山区受多云多雨复杂天气影响,遥感技术应用于种植结构信息提取具有较大难度,基于Sentinel-1进行作物识别在精准农业中具有独特优势,可及时、准确地掌握区域主要作物种植信息,对于制定农业政策和指导农业生产具有重要意义。文章以贵州省关岭县作为研究区,采用2020年Google Earth影像、4—8月时序Sentinel-1数据和无人机遥感数据,利用D_LinkNet模型进行地块提取,基于LightGBM模块进行种植结构分类,结合地理探测器探究研究区主要作物空间分异特征及种植结构空间分异的影响机理。研究表明: ①关岭县作物分布呈“西北多,东南少”格局,空间分布不均衡; ②因子交互作用的影响均比单一因子影响程度大,交通区位与排涝能力是影响耕地分布的主要因素,次要因子为高程与交通区位等因子; ③作物种植结构提取结果与统计年鉴比例一致,混淆矩阵总体精度为0.87,Kappa系数为0.83。研究结果有利于理解喀斯特山区不同粮食作物种植结构空间分异的形成机理及其差异,为种植结构优化调整、影响因素分析提供科学依据。
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关键词:
- 喀斯特 /
- 耕地利用 /
- 种植结构 /
- Sentinel-1 /
- 空间分异
Abstract: Karst mountainous areas are influenced by complex cloudy and rainy weather. This brings great difficulties to the extraction of planting structure information using the remote sensing technology. Sentinel-1-based crop identification has unique advantages in precision agriculture. It can obtain the information on regional main crops in time and accurately, thus playing a significant role in formulating agricultural policies and guiding agricultural production. This study investigated Guanling County based on Google images in 2020, Sentinel-1 time series data from April to August, and UAV remote sensing data. First, the plots were extracted using the D_LinkNet model. Then, the planting structures were classified based on the LightGBM module. Finally, the spatial differentiation characteristics of main crops and the influencing mechanism of planting structures in the study area were explored combined with geographic detectors. The results are as follows. ① The crops in Guanling County showed an uneven spatial distribution pattern of more crops in the northwest and less crops in the southeast. ② The influence of factor interaction was greater than that of single factors. The distribution of cultivated land was mainly influenced by traffic location and drainage capacity, followed by factors such as elevation and traffic location. ③ The extraction results of crop planting structures are consistent with the proportions shown in the statistical yearbook, with confusion-matrix overall precision of 0.87 and Kappa coefficient of 0.83. The results can help understand the formation mechanisms and differences in the spatial differentiation of different crop planting structures in Karst mountainous areas. Therefore, this study can provide a scientific basis for the optimization and adjustment of planting structures and the analysis of influencing factors.-
Key words:
- Karst /
- cultivated land utilization /
- planting structure /
- Sentinel-1 /
- space differentiation
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