姓名 | 施开放 | 
|
性别 | 男 |
| 职称 | 教授 |
| 最终学历 | 研究生 |
学位 | 博士 |
E-mail | shikf@ahnu.edu.cn |
通讯地址 |
安徽省芜湖市弋江区yl6809永利官网yl6809永利官网（行政楼）203-1，邮编：241002 |
个人简介：
施开放，男，汉族，教授，安徽省马鞍山市人。2017年6月毕业于华东师范大学，获地图学与地理信息系统专业博士学位；其中，2014年—2016年国家公派赴澳大利亚CSIRO Land and Water进行博士联合培养，主要从事城市社会经济感知与模拟、夜间灯光遥感等方面的研究与教学工作。近年来，先后主持安徽省高等学校科研计划杰出青年基金项目、国家自然科学青年基金项目、安徽省研究生教育教学改革研究重点项目等10余项。以第一或通讯作者在The Innovation、Landscape and Urban Planning、Earth’s Future、Computers, Environment and Urban Systems、IEEE Transactions on Geosciences and Remote Sensing等国内外核心期刊上发表学术论文80余篇，其中ESI高被引论文6篇（包括1篇热点论文）。连续入选2022、2023年Elsevier全球前2%顶尖科学家榜单，获2023年安徽省教学成果奖（二等奖）、首届川渝科技学术大会优秀论文一等奖，担任Nature Sustainability、Nature Cities等国内外几十个核心期刊审稿人。
研究方向：城市社会经济感知与模拟
聚焦于城市社会经济感知与模拟的理论、方法与实践，主要学术贡献包括：构建了城市社会经济智能感知模型，实现了城市结构从外在空间形态到内在社会经济形态的精细化识别与模拟；揭示了城市社会经济的空间演化模式及其碳排放效应；构建了夜光非系统性噪声与时序性校正模型，显著提升了主流异源夜间灯光数据的时空可比性和延伸性。研究成果被世界银行、NASA、美国国家经济研究局、世界经济论坛等机构广泛采纳和应用。构建的长时序高空间分辨率社会经济指标数据集，公开发布在国家地球系统科学数据中心和Harvard Dataverse等国内外数据共享网站上，并被广泛下载和引用。
课题组微信公众号（Urban social economy感知与模拟）：
工作经历：
l 2023年12月—至今，yl6809永利官网yl6809永利官网，教授
l 2023年1月—2023年11月，yl6809永利官网yl6809永利官网，副教授（高层次人才引进）
l 2020年12月—至今，武汉大学测绘遥感信息工程国家重点实验室，博士后（合作导师：李德仁院士）
l 2017年7月—2022年12月，西南大学地理科学学院，副教授（博士毕业破格引进）
近年来课程承担与学生培养:
l 本科生课程：《遥感地学分析》《GIS应用实习》《遥感概论》
l 研究生课程：《地理建模》《地理信息系统》
l 指导多名本科生承担国家及省级大学生创新创业训练计划项目
l 指导多名本科生连续多年获得优秀本科毕业论文（一等奖或二等奖）
l 指导多名研究生获得国家奖学金、尹兴明奖学金、优秀毕业生称号等
l 指导多名本科生和研究生在专业领域顶级学术期刊上发表学术论文
学术兼职与社会服务：
l 国家自然科学基金通讯评审专家、重庆市自然科学基金通讯评审专家、中国地理学会会员、IEEE Member
l Associate Editor: Frontiers in Remote Sensing (ESCI/EI)
l Editorial Board Member: Land (SSCI/SCI)
l Guest Editor: IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing (SCI), Remote Sensing (SCI), Land (SSCI/SCI), 遥感技术与应用, 高原气象
l 部分期刊审稿人：Nature Sustainability, Nature Cities, One Earth, Remote Sensing of Environment, ISPRS Journal of Photogrammetry and Remote Sensing, IEEE Transactions on Geoscience and Remote Sensing, Journal of Remote Sensing, International Journal of Applied Earth Observations and Geoinformation, GIScience and Remote Sensing, International Journal of Digital Earth, International Journal of Remote Sensing, Remote Sensing Letters, IEEE Geoscience and Remote Sensing Letters, Transactions in GIS, Geo-spatial Information Science, Science China Earth Sciences, Annals of the AAG, Cities, Applied Geography, Sustainable Cities and Society, Environment and Planning B, Scientific Data, Environmental Impact Assessment Review, Urban Climate, Applied Energy, Building and Environment, Resources Conservation & Recycling, Journal of Environmental Management, Gondwana Research, Environment International, Environmental Pollution, Applied Spatial Analysis and Policy, Regional Environmental Change, Land Degradation & Development, 测绘学报, 武汉大学学报（信息科学版）, 同济学报（自然科学版）, 地球信息科学学报, 地理研究, 地理科学, 地球科学进展, 中国管理科学等
主持科研项目：
l 安徽省高等学校科研计划杰出青年项目（2023AH020029），2023-2026年，（主持，在研）
l 安徽省社科规划青年项目（AHSKYQ2023D062），2024-2026年，（主持，在研）
l 安徽省研究生教育教学改革研究重点项目（2023jyjxggyjY058），2024-2025年，（主持，在研）
l 国家自然科学青年基金项目（42101345），2022-2024年，（主持，在研）
l 重庆市社会科学规划项目（2021NDQN39），2021-2023年，（主持，结题）
l 中央高校基本科研业务基金重点项目（自然科学）（XDJK2020B008），2020-2021年，（主持，结题）
l 教育部人文社会科学研究青年基金项目（18XJC790011），2018-2021年，（主持，结题）
l 中央高校基本科研业务基金一般项目（自然科学）（XDJK2018C015），2018-2019年，（主持，结题）
l 重庆市社会科学规划项目（2018BS55），2018-2020年，（主持，结题）
l 西南大学引进人才计划基金项目（自然科学）（SWU118102），2018-2020年，（主持，结题）
部分第一/通讯作者学术论文（*为通讯作者）
Google Citation: https://x.glgoo.top/citations?user=4doiZ7cAAAAJ&hl=zh-CN
ORCID: https://orcid.org/0000-0001-9047-2885
54. Jiang, L., Wu, Y., Wang, J., Han, H*., Shi, K*., 2024. A nonparametric approach for identifying and evaluating urban polycentric spatial structure in China using remote sensing nighttime light and point of interest data. GIScience and Remote Sensing 61 (1): 2383461.
53. Liu, L., Shen, J., Chang, Z., Shi, K*., 2024. A nighttime light remote sensing based urban spatial structure revealing urban spatial polycentric structure affected by haze pollution. IEEE Geoscience and Remote Sensing Letters 21: 3003305.
52. Chang, Z., Liu, L., Ma, J., Cao, W., Cui, Y., Shi, K*., 2024. Hillside urban expansion exacerbates nature habitat landscape fragmentation in China. International Journal of Digital Earth 17 (1): 2368095. (TOP期刊)
51. Yu, Y., Liu, L., Chang, Z., Li, Y*., Shi, K*., 2024. Detecting forest fires in Southwest China from remote sensing nighttime lights using the random forest classification model. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 17: 10759-10769. (本科生为第一作者)
50. Xiao, Z., Pan, Y., Jiang, L., Wang, Z*., Shi, K*., 2024. Remote sensing nighttime lights reveal the post-earthquake losses and reconstruction situations in Turkey–Syria earthquake areas. IEEE Geoscience and Remote Sensing Letters 21: 3002405. (本科生为第一作者)
49. Cui, Y., Zhao, R., Zhang, M., Wang, L., Yuan, Y., Duan, X*., Shi, K*., 2024. Rapid socioeconomic growth in Southeast Asia: Evidence from nighttime light observations. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 17: 5294-5302.
48. Shi, K., Pan, Y., Jiang, L., Wang, J., Cui, Y., Dong, T., Ma, J*., Huang, C*., 2024. A nighttime light based urban sprawl model revealing reduced electricity intensity with increasing urban size. IEEE Geoscience and Remote Sensing Letters 21: 2501105.
47. Shi, K., Yu. B., Ma, J., Cao, W., Cui, Y*., 2023. Impacts of slope climbing of urban expansion on global sustainable development. The Innovation 4 (6): 100529. (2023年影响因子33.2)
46. Shi, K., Ma, J., Chen, Z., Cui, Y*., Yu. B*., 2023. Nighttime light remote sensing in characterizing urban spatial structure. The Innovation Geoscience 1 (3): 100043. (当期封面导读)
45. Shi, K., Wu, S., Cui, Y, Liu, S*., 2023. The impact of the COVID-19 on socioeconomic activity exchanges in Himalaya region: A satellite nighttime light perspective. IEEE Geoscience and Remote Sensing Letters 20: 2503105.
44. Yu, Y., Liu, L., Li, Y*., Shi, K*., 2023. Satellite remotely sensed nighttime lights reveal spatiotemporal dynamics of the Ukrainian-Russian conflict. IEEE Geoscience and Remote Sensing Letters 20: 2503005. (本科生为第一作者)
43. Chang, Z., Wu, Y., Liu, L., Shen, J., Shi, K*., 2023. Exploring the correlations between SNPP-VIIRS nighttime light data and population from a multiplescale perspective. IEEE Geoscience and Remote Sensing Letters 20: 3001905.
42. Shi, K., Cui, Y., Liu, S., Wu, Y*., 2023. Global urban land expansion tends to be slope climbing: A remotely sensed nighttime light approach. Earth’s Future 11: e2022EF003384.
41. Shi, K., Liu, G., Zhou, L., Cui, Y., Liu, S., Wu, Y*., 2023. Satellite remote sensing data reveal increased slope climbing of urban land expansion worldwide. Landscape and Urban Planning 235: 104755.
40. Cui, Y., Zha, H., Jiang, L., Zhang, M., Shi, K*., 2023. Luojia 1-01 data outperform Suomi-NPP VIIRS data in estimating CO2 emissions in the service, industrial, and urban residential sectors. IEEE Geoscience and Remote Sensing Letters 20: 3000905.
39. Wu, Y., Shi, K*., Liu, S., Liu, L., 2023. Differentiated effects of morphological and functional polycentric urban spatial structure on carbon emissions in China: An empirical analysis from remotely sensed nighttime light approaches. International Journal of Digital Earth 16 (1): 532-551.
38. Shi, K*., Wu, Y., Liu, S., Chen, Z., Huang, C., Cui, Y*., 2023. Mapping and evaluating global urban entities (2000–2020): A novel perspective to delineate urban entities based on consistent nighttime light data. GIScience and Remote Sensing 60 (1): 2161199. (ESI高被引用论文)
37. Shi, K., Liu, G., Wu, Y*., Cui, Y., 2023. What urban spatial structure is more conducive to reducing carbon emissions? A conditional effect of population size. Applied Geography 151: 102855.
36. Liu, S., Shi, K*., Wu, Y., Cui, Y., 2023. Suburban greening and suburbanization changing surface urban heat island intensity in China. Building and Environment 228: 109906.
35. Shi, K., Wu, Y*., Liu, S. 2022. Slope climbing of urban expansion worldwide: Spatiotemporal characteristics, driving factors and implications for food security. Journal of Environmental Management 324: 116337.
34. Liu, S., Shi, K*., Wu, Y. 2022. Identifying and evaluating suburbs in China from 2012 to 2020 based on SNPP–VIIRS nighttime light remotely sensed data. International Journal of Applied Earth Observation and Geoinformation 114: 103041.
33. Chang, Z., Liu, S., Wu, Y., Shi, K*. 2022. The regional disparity of urban spatial expansion is greater than that of urban socioeconomic expansion in China: A new perspective from nighttime light remotely sensed data and urban land datasets. Remote Sensing 14(17): 4348.
32. Shi, K., Wu, Y., Li, D., Li, X*. 2022. Population, GDP, and carbon emissions as revealed by SNPP-VIIRS nighttime light data in China with different scales. IEEE Geoscience and Remote Sensing Letters 19: 3008005.
31. Liu, S., Shen, J., Liu, G., Wu, Y., Shi, K*. 2022. Exploring the effect of urban spatial development pattern on carbon dioxide emissions in China: A socioeconomic density distribution approach based on remotely sensed nighttime light data. Computers, Environment and Urban Systems 96: 101847.
30. Wu, Y., Li, C., Shi, K*., Liu, S., Chang, Z. 2022. Exploring the effect of urban sprawl on carbon dioxide emissions: An urban sprawl model analysis from remotely sensed nighttime light data. Environmental Impact Assessment Review 93: 106731. (ESI热点论文)
29. Wu, Y., Shi, K*., Chen, Z., Liu, S., Chang, Z. 2022. Developing time-series of improved DMSP-OLS-like data (1992-2019) in China by integrating DMSP-OLS and SNPP-VIIRS. IEEE Transactions on Geoscience and Remote Sensing 60: 4407714.
28. Shi, K., Shen, J., Wu, Y., Tang, X*. 2022. Identifying and quantifying urban polycentric development in China from DMSP-OLS data and urban land datasets. IEEE Geoscience and Remote Sensing Letters 22: 3000805.
27. Liu, S., Shi, K*., Wu, Y., Chang, Z. 2021. Remotely sensed nighttime lights reveal China’s urbanization process restricted by haze pollution. Building and Environment 206: 108350.
26. Shi, K., Shen, J., Wu, Y., Liu, S., Li, L*. 2021. Carbon dioxide (CO2) emissions from the service industry, traffic, and secondary industry as revealed by the remotely sensed nighttime light data. International Journal of Digital Earth 14 (11): 1514-1527.
25. Shi, K*., Wu, Y. Liu, S. 2021. Does China’s city-size distribution present a flat distribution trend: An empirical analysis from DMSP-OLS nighttime light data. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 14: 5171-5179.
24. Shi, K., Wu, Y., Li, L*. 2021. Quantifying and evaluating the effect of urban expansion on the fine particulate matter emissions from fossil fuel combustion in China. Ecological Indicators 125: 107541.
23. Huang, X., Shi, K*., Cui, Y., Li, Y., 2021. A saturated light correction method for DMSP-OLS nighttime stable light data by remote and social sensing data. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 14: 1885-1894. (本科生为第一作者)
22. 伍亿真, 施开放*, 余柏蒗, 李川龙, 2021. 利用NPP-VIIRS夜间灯光遥感数据分析城市蔓延对雾霾污染的影响. 武汉大学学报·信息科学版 46(5): 777-789.
21. Shi, K.; Cui, Y.; Chen, Z., Wu, J., Yu. B*. 2021. NPP-VIIRS nighttime light data have different correlated relationships with fossil fuel combustion carbon emissions from different sectors. IEEE Geoscience and Remote Sensing Letters 18(12): 2062-2066.
20. Cui, Y.; Shi, K*.; Jiang, L., Feng, Q. 2021. Identifying and evaluating the nighttime economy in China by using multisource data. IEEE Geoscience and Remote Sensing Letters 18(11): 1906-1910.
19. Ma, M.; Ge, W*.; Shi, K*. 2021. Airport’s throughput estimation using nighttime light data in China mainland. IEEE Geoscience and Remote Sensing Letters 18(8): 1357-1360.
18. Shi, K*., Shen, J., Wang, L., Ma, M., Cui, Y*. 2020. A multiscale analysis of the effect of urban expansion on PM2.5 concentrations in China: Evidence from multisource remote sensing and statistical data. Building and Environment 174: 106778.
17. Shi, K., Xu, T., Li, Y., Chen, Z., Gong, W., Wu, J., Yu, B*. 2020. Effects of urban forms on CO2 emissions in China from a multi-perspective analysis. Journal of Environmental Management 262: 110300.
16. Li, L.; Chen, Y.; Xu, T.; Meng, L.; Huang, C.; Shi, K*. 2020. Spatial attraction models coupled with Elman Neural Networks for enhancing sub-pixel urban inundation mapping. Remote Sensing 12: 2068.
15. Shi, K., Chang, Z., Chen, Z., Wu, J., Yu, B*. 2020. Identifying and evaluating poverty using multisource remote sensing and point of interest (POI) data: A case study of Chongqing, China. Journal of Cleaner Production 255: 120245.
14. Shi, K., Yu, B*., Zhou, Y., Chen, Y., Yang, C., Chen, Z., and Wu, J. 2019. Spatiotemporal variations of CO2 emissions and their impact factors in China: A comparative analysis between the provincial and prefectural levels. Applied Energy 233-234: 170-181. (ESI高被引用论文)
13. Shi, K., Yang, Q*., Li, Y., and Sun, X. 2019. Mapping and evaluating cultivated land fallow in Southwest China using multisource data. Science of the Total Environment 654: 987-999.
12. Shi, K*., Li, Y., Chen, Y., Li, L., and Huang, C., 2019. How does the urban forms-PM2.5 concentration relationship change seasonally in Chinese cities? A comparative analysis between national and urban agglomeration scales. Journal of Cleaner Production 239: 118088.
11. Shi, K*., Wang, H., Yang, Q., Wang, L., Sun, X., Li, Y*.,2019. Exploring the relationships between urban forms and fine particulate (PM2.5) concentrations in China: A multi-perspective. Journal of Cleaner Production 231: 990-1004.
10. Shi, K., Yang, Q., Fang, G., Yu, B*., Chen, Z., Yang, C., and Wu, J. 2019. Evaluating spatiotemporal patterns of urban electricity consumption within different spatial boundaries: A case study of Chongqing, China. Energy 167: 641-653.
9. Shi, K., Yu, B*., Huang, C., Wu, J and Sun, X. 2018. Exploring spatiotemporal patterns of electric power consumption in countries along the Belt and Road. Energy 150: 847-859.
8. Shi, K*., Chen, Y., Li, L., and Huang, C. 2018. Spatiotemporal variations of urban CO2 emissions in China: A multiscale perspective. Applied Energy 211: 218-229.
7. 孙秀锋, 施开放*, 吴健平, 2018. 县级尺度的重庆市碳排放时空格局动态. 环境科学 39(6): 466-476.
6. Shi, K*., Huang, C., Chen, Y and Li, L., 2018. Remotely sensed nighttime lights reveal increasing human activities in protected areas of China mainland. Remote Sensing Letters 9(5): 468-477.
5. Shi, K., Chen, Y., Yu, B*., Xu, T., Yang, C., Li, L., Huang, C., Chen, Z., Liu, R., and Wu, J., 2016. Detecting spatiotemporal dynamics of global electric power consumption using DMSP-OLS nighttime stable light data. Applied Energy 184: 450-463.
4. Shi, K., Chen, Y., Yu, B*., Xu, T., Chen, Z., Liu, R., Li, L., and Wu, J., 2016. Modeling spatiotemporal CO2 (carbon dioxide) emission dynamics in China from DMSP-OLS nighttime stable light data using panel data analysis. Applied Energy 168: 523-533. (ESI高被引用论文)
3. Shi, K., Yu, B*., Hu, Y., Huang, Y., Huang, C., Chen, Y., Chen, Z., and Wu, J., 2015. Modeling and mapping total freight traffic in China using NPP-VIIRS nighttime light composite data. GIScience and Remote Sensing 52(3): 274-289.
2. Shi, K., Huang, C., Yu, B*., Yin, B., Huang, Y., and Wu, J., 2014. Evaluation of NPP-VIIRS nighttime light composite data for extracting built-up urban areas. Remote Sensing Letters 5(4): 358-366. (ESI高被引用论文)
1. Shi, K., Yu, B*., Huang, Y., Hu, Y., Yin, B., Chen, Z., Chen, L., and Wu, J., 2014. Evaluating the ability of NPP-VIIRS nighttime light data to estimate the gross domestic product and the electric power consumption of China at multiple scales: A comparison with DMSP-OLS data. Remote Sensing 6(2): 1705-1724. (ESI高被引用论文)
授权专利及软件著作权：
l 国家发明专利，ZL 2020 1 1238915.8，授权日期2023年3月14日
l 实用新型专利，ZL 2019 2 1766044. X，授权日期2020年7月14日
荣誉与获奖情况：
l 安徽省教学成果二等奖（2023jxcgj077，3/9），2024
l yl6809永利官网第四届全国高校教师教学创新大赛校赛二等奖，2024年
l 入选Elsevier全球前2%顶尖科学家榜单，2023年
l 第七届全国高校GIS青年教师讲课竞赛二等奖，2023年
l 入选Elsevier全球前2%顶尖科学家榜单，2022年
l 自然资源部高层次科技创新人才工程科技创新团队成员，2022年
l 西南大学地理科学学院尹兴明教师奖（科研奖），2021年
l 西南大学优秀本科毕业论文指导奖（多个一、二等奖），2021年
l 西南大学实习先进工作者，2021年
l 首届川渝科技学术大会优秀论文一等奖（1/5，交叉学科排名第一），2020年
l 重庆英才创新创业示范团队成员，2020年
l 重庆市研究生导师团队成员，2019年
l 重庆市科学技术协会自然科学优秀论文奖（1/4），2019年
l 西南大学优秀本科毕业论文指导奖（二等奖），2019年
发布的部分数据集：
l 1992-2023年中国长时序夜间灯光遥感数据集（1 km）
（http://geodata.nnu.edu.cn/data/datadetails.html?dataguid=54416522887785&docid=16;
https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/GIYGJU）
l 2012-2020年中国城市郊区范围数据集（500 m）
（https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/M5EED5；
http://geodata.nnu.edu.cn/data/datadetails.html?dataguid=232582871367490&docid=1）
l 2000-2020年全球城市实体数据集（500 m）
（https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/79CRQJ；
http://geodata.nnu.edu.cn/data/datadetails.html?dataguid=127029753298757&docid=11）
l 2021-2022年中国西南地区森林火灾空间分布数据集（500 m）
（https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/QIUAE3）
l 2012-2018年中国城市经济密度分布数据集（500 m）
（http://geodata.nnu.edu.cn/data/datadetails.html?dataguid=30242668655895&docid=0;
https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/EAMMGU）
l 1997-2012年中国二氧化碳排放数据集（1 km）
（http://nnu.geodata.cn:8008/data/datadetails.html?dataguid=49936583368636&docid=2）
l 1992-2013年全球电力消耗数据集（1 km）
（http://nnu.geodata.cn:8008/data/datadetails.html?dataguid=8155134180139&docid=3）
l 2000-2012全球城市建成区数据集（1 km）
（http://nnu.geodata.cn:8008/data/datadetails.html?dataguid=3757086314903&docid=1）
