基于卫星遥感技术的区域经济发展模型构建

doi:10.6046/gtzyyg.2020.02.29

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针对区域经济发展调查中传统方法费时费力、数据缺乏客观性的问题,从区域经济发展引起地球表面形态变化的角度出发,借助卫星遥感技术优势,构建模型,以便更加客观、真实地了解区域经济发展形势。首先,基于卫星遥感数据,获取不同时相地球表面形态变化和土地利用信息; 其次,分析各土地利用类型与各区域经济指标之间的相关性,优选敏感因子; 然后,结合社会调查数据,构建区域经济发展模型; 最后,进行精度评价,以验证模型的有效性和适用性。以舟山群岛为例的研究结果表明,建设用地面积是与各经济指标相关性最强的敏感因子,与国内生产总值(gross domestic product,GDP)、第一产业总值(primary industry product,PIP)、第二产业总值(secondary industry product,SIP)和第三产业总值(tertiary industry product,TIP)的相关系数分别为0.959 1,0.939 0,0.954 6和0.957 3; 遥感数据与社会调查数据相结合的区域经济发展模型简单清晰,精度较高,平均决定系数R²为0.979 5。本研究为区域经济发展预测和经济数据纠偏提供了一种新思路,为观测区域经济活动及其影响提供了一种新途径,对于了解区域经济发展,调整和修正统计数据具有实用意义。

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	古海玲
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关键词 ：土地利用与覆被变化, 卫星遥感技术, 区域经济发展, 模型构建, 舟山群岛

Abstract：

In order to break through the time-consuming and laborious limitations of traditional regional economic development surveys, the authors built some regional economic development models by virtue of the advantages of remote sensing technology. First, based on multi-source and multi-temporal satellite remote sensing data, the authors obtained surface morphological changes and land use information, analyzed the correlation between land use types and regional economic indicators, optimized sensitive factors, then combined the social survey data to build a regional economic development model and finally performed an accuracy evaluation to verify the validity and applicability of the model. Zhoushan Islands were selected as the research area to carry out verification experiments. The experimental results show that the construction land area is the most sensitive factor related to various economic indicators, and the correlation coefficients with GDP, PIP, SIP and TIP are respectively 0.959 1, 0.939 0, 0.954 6 and 0.957 3. The average determination coefficient R² of the regional economic development model built with the survey data is 0.979 5. The results obtained by the authors provide a new way of thinking for regional economic development prediction and economic data correction and also provide a possibility for humans to observe economic activities and their impact. The model built in this study is simple and clear yet with high precision, and thus is of great significance for understanding regional economic development as well as adjusting and correcting statistical data.

Key words： land use and cover change satellite remote sensing technology regional economic development model construction Zhoushan Islands

收稿日期: 2019-06-06 出版日期: 2020-06-18

TP79

基金资助:浙江省省属高校科研院所基本科研业务费专项(2019J00003);国家自然科学基金项目“基于空间数据挖掘的高分辨率遥感图像水上桥梁目标识别与损毁评估”(41701447)

通讯作者: 陈超

作者简介: 古海玲(1997-),女,本科生,主要从事遥感技术应用研究。Email: guhailing_zj@163.com。

引用本文:

古海玲, 陈超, 芦莹, 褚衍丽. 基于卫星遥感技术的区域经济发展模型构建[J]. 国土资源遥感, 2020, 32(2): 226-232.
Hailing GU, Chao CHEN, Ying LU, Yanli CHU. Construction of regional economic development model based on satellite remote sensing technology. Remote Sensing for Land & Resources, 2020, 32(2): 226-232.

链接本文:

https://www.gtzyyg.com/CN/10.6046/gtzyyg.2020.02.29 或 https://www.gtzyyg.com/CN/Y2020/V32/I2/226

Tab.1 研究区数据获取情况

Fig.1 本文技术路线

Tab.2 各土地利用类型面积

Fig.2 分类结果

指标	Ⅰ	Ⅱ	Ⅲ	Ⅳ	Ⅴ	lg(Ⅰ)	lg(Ⅱ)	lg(Ⅲ)	lg(Ⅳ)	lg(Ⅴ)	e⁽^Ⅰ^')	e⁽^Ⅱ^')	e⁽^Ⅲ^')	e⁽^Ⅳ^')	e⁽^Ⅴ^')
GDP	-0.545 5	0.645 8	0.919 4	-0.172 8	0.782 2	-0.546 3	0.607 2	0.842 2	-0.113 2	0.777 4	-0.529 9	0.662 4	0.943 9	-0.222 1	0.808 0
PIP	-0.443 3	0.621 4	0.917 2	-0.263 1	0.762 5	-0.443 5	0.581 9	0.850 0	-0.202 5	0.756 0	-0.432 6	0.643 4	0.939 8	-0.310 6	0.801 7
SIP	-0.614 0	0.647 8	0.926 8	-0.144 5	0.757 6	-0.616 1	0.617 0	0.860 8	-0.084 3	0.754 0	-0.589 3	0.655 4	0.940 9	-0.197 3	0.773 6
TIP	-0.526 1	0.647 4	0.897 8	-0.155 1	0.7943	-0.526 1	0.603 1	0.809 0	-0.098 0	0.789 2	-0.515 6	0.668 5	0.931 0	-0.201 6	0.822 9
人均 GDP	-0.547 0	0.646 1	0.919 5	-0.171 7	0.782 3	-0.547 8	0.607 6	0.842 4	-0.112 1	0.777 5	-0.531 4	0.662 4	0.943 9	-0.221 0	0.807 9
常住人口	-0.463 9	0.616 0	0.962 8	-0.4057	0.622 8	-0.468 0	0.615 4	0.956 2	-0.329 6	0.618 3	-0.430 3	0.605 0	0.942 9	-0.469 6	0.647 5
e⁽^GDP^')	-0.453 5	0.7055	0.881 5	-0.267 4	0.725 1	-0.4545	0.691 4	0.807 4	-0.196 2	0.719 0	-0.437 8	0.704 7	0.907 0	-0.326 3	0.762 2
e⁽^PIP^')	-0.404 4	0.616 2	0.870 1	-0.224 9	0.781 9	-0.403 2	0.568 2	0.787 0	-0.168 4	0.774 9	-0.403 0	0.645 4	0.905 1	-0.267 5	0.824 9
e⁽^SIP^')	-0.574 6	0.643 2	0.901 9	-0.129 9	0.779 7	-0.575 8	0.605 4	0.822 2	-0.071 2	0.775 4	-0.555 9	0.657 6	0.927 5	-0.179 8	0.801 5
e⁽^TIP^')	-0.463 0	0.630 5	0.856 7	-0.151 7	0.799 3	-0.461 8	0.580 6	0.758 7	-0.097 5	0.793 4	-0.460 9	0.657 7	0.899 2	-0.193 6	0.834 1
e^(人均^GDP^')	-0.492 7	0.635 0	0.880 8	-0.159 1	0.7952	-0.492 3	0.589 6	0.791 1	-0.102 4	0.789 7	-0.485 3	0.658 1	0.916 4	-0.204 1	0.826 9
e^{(常住人口')}	-0.502 7	0.630 9	0.967 1	-0.3289	0.691 9	-0.505 7	0.617 0	0.934 9	-0.257 1	0.687 3	-0.474 6	0.629 4	0.962 7	-0.388 6	0.716 2
lg(GDP)	-0.459 3	0.5901	0.947 9	-0.431 7	0.563 1	-0.4640	0.599 9	0.959 1	-0.361 5	0.558 8	-0.422 0	0.571 2	0.914 8	-0.491 8	0.585 9
lg(PIP)	-0.372 5	0.565 5	0.925 4	-0.486 1	0.543 5	-0.376 9	0.575 0	0.939 0	-0.413 2	0.537 6	-0.337 5	0.551 8	0.896 9	-0.547 2	0.579 5
lg(SIP)	-0.492 3	0.594 1	0.942 4	-0.405 9	0.554 6	-0.497 0	0.606 3	0.954 6	-0.337 7	0.551 1	-0.454 2	0.571 5	0.906 4	-0.465 5	0.572 0
lg(TIP)	-0.5024	0.620 1	0.959 9	-0.387 5	0.602 7	-0.506 3	0.622 9	0.957 3	-0.321 0	0.598 7	-0.468 8	0.604 1	0.933 8	-0.445 3	0.623 3
lg(人均GDP)	-0.464 2	0.591 0	0.949 4	-0.426 8	0.566 8	-0.468 7	0.600 1	0.959 7	-0.357 2	0.562 6	-0.427 2	0.572 4	0.916 7	-0.486 5	0.589 2
lg(常住人口)	-0.450 7	0.610 9	0.958 3	-0.425 4	0.603 2	-0.455 0	0.613 7	0.958 1	-0.348 2	0.598 7	-0.415 9	0.597 5	0.934 8	-0.490 3	0.628 2

Tab.3 土地利用类型面积与各经济指标间的相关系数表

Tab.4 模型构建结果

Tab.5 模型精度评价结果

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