以安徽省亳州市为研究区域，通过可视化方式展现了几种不同用途建筑灯光在2个时相的强度差别，继而对灯光面积和不透水层面积进行线性回归分析和交并比分析，获得城市与乡镇地区灯光分布的差异及变化情况。结果显示：1)不同建筑类型在不同时间的灯光强度存在差异；2)因空间分辨率高，SDGSAT-1微光影像能够部分区分城市区域的主干道路灯光与建筑灯光，城市区域的灯光大部分由道路发出；3)灯光面积与不透水层面积的线性回归在城市区域能够产生较好的拟合效果，乡镇地区的灯光面积因不易被传感器接收而难以与不透水层面积建立良好的拟合关系；4)亳州市各街道、乡镇灯光面积与不透水层面积的交并比与GDP相关。
王昭
,
闫冬梅
,
唐娉
,
张正
. 乡镇尺度SDGSAT-1微光影像灯光分布与不透水层的相关性分析[J]. 中国科学院大学学报, 2025
, 42(4)
: 528
-537
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DOI: 10.7523/j.ucas.2023.074
Taking Bozhou City, Anhui Province as the research area, the differences in the light intensity of several buildings with different uses in two phases were shown in a visualized way. The light area and the impervious area were analyzed by linear regression and IoU (intersection over union) to obtain the differences and changes in light distribution in urban and rural areas. The results show that: 1) Different building types have different light intensities at different times. 2) Due to the high spatial resolution, SDGSAT-1 low-light images can partially distinguish between the main road lights and building lights in urban areas. 3) The linear regression of the light area and the area of the impermeable layer can produce a good fitting effect in the urban area, but the light in the township area can not be well fitted with the area of the impermeable layer because it is not easy to be received by the sensor. 4) The IoU of light area and impervious area in the towns of Bozhou is related to GDP.
[1] 李德仁, 李熙. 论夜光遥感数据挖掘[J]. 测绘学报, 2015, 44(6):591-601. DOI: 10.11947/j.AGCS.2015.20150149.
[2] Forbes D J. Multi-scale analysis of the relationship between economic statistics and DMSP-OLS night light images[J]. GIScience & Remote Sensing, 2013, 50(5): 483-499. DOI: 10.1080/15481603.2013.823732.
[3] Doll C N H, Muller J P, Morley J G. Mapping regional economic activity from night-time light satellite imagery[J]. Ecological Economics, 2006, 57(1): 75-92. DOI: 10.1016/j.ecolecon.2005.03.007.
[4] 陈晋, 卓莉, 史培军, 等. 基于DMSP/OLS数据的中国城市化过程研究: 反映区域城市化水平的灯光指数的构建[J]. 遥感学报, 2003, 7(3): 168-175, T001. DOI: 10.3321/j.issn: 1007-4619.2003.03.002.
[5] Li X, Li D. Can night-time light images play a role in evaluating the Syrian Crisis?[J]. International Journal of Remote Sensing, 2014, 35(18): 6648-6661. DOI: 10.1080/01431161.2014.971469.
[6] 李峰, 米晓楠, 刘军,等.基于NPP-VIIRS夜间灯光数据的北京市GDP空间化方法[J]. 国土资源遥感, 2016, 28(3):19-24. DOI: 10.6046/gtzyyg.2016.03.04.
[7] 李翔. 基于夜光遥感数据的中国2005—2015年居民收入时空变化与驱动力研究[D]. 南京:南京大学, 2018.
[8] Li D R, Zhang G, Shen X, et al. Design and processing night light remote sensing of LJ-1 01 satellite[J]. National Remote Sensing Bulletin, 2019, 23(6): 1011-1022. DOI: 10.11834/jrs.20199327.
[9] 欧晓华, 梁保平.“珞珈一号”夜光数据在漓江流域城镇信息提取中的应用[J].测绘与空间地理信息, 2022, 45(10):18-22. DOI: 10.3969/j.issn.1672-5867.2022.10.007.
[10] 娄格, 陈秋晓. 基于“珞珈一号”夜光遥感数据融合的乡村人口空间化方法研究[J].建筑与文化, 2021(1):74-75. DOI: 10.19875/j.cnki.jzywh.2021.01.021.
[11] Guo H D, Dou C Y, Chen H Y, et al. SDGSAT-1: the world’s first scientific satellite for sustainable development goals[J]. Science Bulletin, 2023, 68(1): 34-38. DOI: 10.1016/j.scib.2022.12.014.
[12] Zhao M, Cheng W M, Zhou C H, et al. GDP spatialization and economic differences in South China based on NPP-VIIRS nighttime light imagery[J]. Remote Sensing, 2017, 9(7): 673. DOI: 10.3390/rs9070673.
[13] Zhang X, Liu L Y, Zhao T T, et al. GISD30: global 30 m impervious-surface dynamic dataset from 1985 to 2020 using time-series Landsat imagery on the Google Earth Engine platform[J]. Earth System Science Data, 2022, 14(4): 1831-1856. DOI: 10.5194/essd-14-1831-2022.
