摘要:
对中国三大经济带的经济发展状况进行综合性分析，对于了解不同经济带间的经济发展差异、进行政策评估与制定、提升人民生活水平等有着重要的意义。遥感影像可以从宏观尺度上提取区域内的土地利用、城市建设、发展活跃度等数据及空间分布信息，在区域经济研究中有着不可替代的作用。将多源遥感数据应用于三大经济带发展研究能够有效利用区域宏观特征，从不同方面探索整体发展特点。本文使用Sentinel-2数据、NPP/VIIRS夜光数据及人口、国民生产总值数据，提取了能够反映区域经济发展状况的特征，并通过构建基于层次分析法的区域经济评分体系综合分析三大经济带中代表省份的经济发展状况，为了解三大经济带发展现状与差异提供有力的数据支撑。结果表明，三个研究时段内江苏省区域经济得分为0.8411、0.9047、0.9894，湖北省得分为0.6796、0.6924、0.7527，宁夏回族自治区得分为0.3852、0.4218、0.4767，同时综合其他数据发现江苏省与湖北省经济发展基础扎实且整体持续向好，而以宁夏回族自治区为代表的西部经济带的经济发展状况仍旧落后于其他地区但发展增速持续提升。本研究将多源遥感数据和社会经济调查数据有效地结合并应用于区域经济研究，能够为研究区域经济发展状况提供科学的方法参考。
Abstract:
Objectives: A comprehensive analysis of the economic development of China's three major economic belts is crucial for understanding the disparities in economic development among different economic belts, conducting policy evaluations and formulation, and improving people's living standards. Remote sensing imagery can extract data and spatial distribution information on land use, urban construction, and development activity from a macroscopic perspective, playing an irreplaceable role in regional economic research. Methods: In response to the current lack of studies utilizing multi-source remote sensing data for regional economic analysis, this paper proposes a comprehensive evaluation and analysis method for assessing regional economic development. This method employs Sentinel-2 data, NPP/VIIRS night-time light data, as well as population and Gross Domestic Product (GDP) data, extracting features that reflect regional economic development from different perspectives. By constructing a regional economic scoring system based on the Analytic Hierarchy Process (AHP), the method provides a comprehensive analysis of the economic development status of representative regions within economic belts. This approach offers strong data support for understanding the current state and disparities among the three major economic belts. Results: The results show that during the three study periods, the regional economic scores for Jiangsu Province are 0.8411, 0.9047, and 0.9894, for Hubei Province are 0.6796, 0.6924, and 0.7527, and for the Ningxia Hui Autonomous Region are 0.3852, 0.4218, and 0.4767 respectively. Simultaneously, comprehensive analysis with other data reveals that the economic foundations of Jiangsu Province and Hubei Province are solid and consistently improving overall, while the economic development of the western economic belt represented by the Ningxia Hui Autonomous Region still lags behind other regions but with a continuous increase in development momentum. Conclusions: This study effectively integrates multi-source remote sensing data and socio-economic survey data, applying them to regional economic research. This approach provides a scientific methodological reference for studying regional economic development.
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