扫 描 看 全 文
Quote
郑嘉豪，孙超，林昀，李璐，刘永超.2023.基于Landsat像元级时间序列的海岸带盐沼植被分类.遥感学报，27（6）： 1400-1413
Zheng J H，Sun C，Lin Y，Li L and Liu Y C. 2023. Classification of salt marsh vegetation based on pixel-level time series from Landsat images. National Remote Sensing Bulletin， 27（6）：1400-1413
盐沼是最具生态价值且最为脆弱的生态系统之一，及时、精确地监测盐沼植被分布对于海岸带生态管理和保护尤为重要。随着多源遥感数据不断积累，时间序列方法日益成为海岸带资源监测的重要手段。然而，由于云雨天气频发，海岸带影像可用性较差，如何有效构建时间序列仍存在较大挑战。本研究耦合多源Landsat影像，以长三角典型滨海湿地为研究区，构建像元级时间序列的XGBoost分类模型，探讨盐沼植被精细识别的可行性与稳定性。研究结果表明：（1）通过相互定标耦合多源影像成效显著，不但提高了影像可用性，还减小了不同传感器之间的光谱反射率差异。（2）基于像元级时间序列方法的盐沼植被分类效果较好，研究区内盐沼植被平均总体分类精度可达81.50%，平均Kappa系数为0.758，对于长三角区域分布广泛的海三棱藨草和互花米草尤为优良。（3）相较于单一时相分类方法，像元级时间序列分类方法的年际绝对均值误差保持小于3.88%，稳定性较好，有望应用在盐沼植被动态变化监测中，为中国海岸带资源高效管理提供遥感技术支持。
Salt marshes are the world’s most valuable and vulnerable ecosystem. Thus, accurate and timely monitoring of the distribution of salt marsh vegetation is essential. With the accumulation of multi-source remote sensing imagery, the time-series method has increasingly become important for monitoring coastal areas. However, effectively constructing time series is still challenging as the number of available observations is relatively low owing to the frequent cloudy weather in the coastal areas. In this study, we coupled multi-sourced Landsat images and constructed a pixel-level time series with XGBoost. Based on this, the feasibility and stability of classifying salt marsh vegetation were tested using the three typical sites in the Yangtze River Delta. Results showed that (1) Inter-calibration for multi-sourced images was necessary for not only improving the availability of images but also reducing the spectral differences among sensors. (2) The performance of salt marsh vegetation classification based on the pixel-level time series was favorable, reflected by 81.50% as the mean overall accuracy and 0.755 as the Kappa coefficient. The classification results were excellent, particularly for the widely distributed ,Suaeda salsa, and ,Spartina alterniflora, in the Yangtze River Delta. (3) Compared with the single-phrase classifications, the pixel-level time series–based classifications were stable, evidenced by an inter-annual absolute mean error lower than 3.27%. Therefore, our proposed method is expected for dynamic monitoring of salt marsh vegetation, which facilitates managing coastal resources and implementing ecological conservation effectively of China’s coasts.
遥感分类盐沼植被Landsat影像像元级时间序列XGBoost长三角丹顶鹤自然保护区九段沙湿地杭州湾南岸湿地
remote sensing classificationsalt marsh vegetationLandsatpixel-level time-seriesXGBoostYangtze River DeltaRed-crowned Crane Nature ReserveJiuduansha WetlandSouthern Coastal Wetland in Hangzhou Bay
Ai J Q, Gao W, Gao Z Q, Shi R H and Zhang C. 2017. Phenology-based Spartina alterniflora mapping in coastal wetland of the Yangtze Estuary using time series of GaoFen satellite no. 1 wide field of view imagery. Journal of Applied Remote Sensing, 11(2): 026020 [DOI: 10.1117/1.JRS.11.026020http://dx.doi.org/10.1117/1.JRS.11.026020]
Chen G, Zhong C R, Li M Y, Yu Z, Liu X Y and Jia M M. 2022. Disturbance of mangrove forests in Guangxi Beilun Estuary during 1990-2020. National Remote Sensing Bulletin, 26(6): 1112-1120
陈高, 钟才荣, 李明玉, 余洲, 刘心雨, 贾明明. 2022. 1990年—2020年广西北仑河口红树林扰动研究. 遥感学报, 26(6): 1112-1120 [DOI: 10.11834/jrs.20221579http://dx.doi.org/10.11834/jrs.20221579]
Chen T Q and Guestrin C. 2016. XGBoost: a scalable tree boosting system//Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. San Francisco: ACM: 785-794 [DOI: 10.1145/2939672.2939785http://dx.doi.org/10.1145/2939672.2939785]
Cheng Q, Chen Y F, Li S D and Xu J F. 2016. Research on plant species diversity in estuarine wetland of Hangzhou bay based on the GF-1 satellite and in situ data. Journal of Natural Resources, 31(11): 1938-1948
程乾, 陈奕霏, 李顺达, 徐俊锋. 2016. 基于高分1号卫星和地面实测数据的杭州湾河口湿地植物物种多样性研究. 自然资源学报, 31(11): 1938-1948 [DOI: 10.11849/zrzyxb.20160056http://dx.doi.org/10.11849/zrzyxb.20160056]
Hou X Y and Xu X L. 2011. Spatial patterns of land use in coastal zones of China in the early 21st century. Geographical Research, 30(8): 1370-1379
侯西勇, 徐新良. 2011. 21世纪初中国海岸带土地利用空间格局特征. 地理研究, 30(8): 1370-1379 [DOI: 10.11821/yj2011080003http://dx.doi.org/10.11821/yj2011080003]
Hou X Y, Zhang H, Li D, Hou W and Song Y. 2018. Development trend, environmental and ecological impacts, and policy recommendations for Bohai Sea reclamation. Acta Ecologica Sinica, 38(9): 3311-3319
侯西勇, 张华, 李东, 侯婉, 宋洋. 2018. 渤海围填海发展趋势、环境与生态影响及政策建议. 生态学报, 38(9): 3311-3319 [DOI: 10.5846/stxb201706261145http://dx.doi.org/10.5846/stxb201706261145]
Hu Y K, Tian B, Yuan L, Li X Z, Huang Y, Shi R H, Jiang X Y, Wang L H and Sun C. 2021. Mapping coastal salt marshes in China using time series of Sentinel-1 SAR. ISPRS Journal of Photogrammetry and Remote Sensing, 173: 122-134 [DOI: 10.1016/j.isprsjprs.2021.01.003http://dx.doi.org/10.1016/j.isprsjprs.2021.01.003]
Huang H M, Zhang L Q and Yuan L. 2007. The spatio-temporal dynamics of salt marsh vegetation for Chongming Dongtan National Nature Reserve, Shanghai. Acta Ecologica Sinica, 27(10): 4166-4172
黄华梅, 张利权, 袁琳. 2007. 崇明东滩自然保护区盐沼植被的时空动态. 生态学报, 27(10): 4166-4172 [DOI: 10.3321/j.issn:1000-0933.2007.10.025http://dx.doi.org/10.3321/j.issn:1000-0933.2007.10.025]
Jiang Z, Huete A R, Didan K and Miura T. 2008. Development of a two-band enhanced vegetation index without a blue band. Remote sensing of Environment, 112(10): 3833-3845 [DOI: 10.1016/j.rse.2008.06.006http://dx.doi.org/10.1016/j.rse.2008.06.006]
Jones J W, Starbuck M J and Jenkerson C B. 2013. Landsat Surface Reflectance Quality Assurance Extraction (version 1.7). US Geological Survey
Li F X, Ding D D, Chen Z J, Chen H H, Shen T, Wu Q L and Zhang C Y. 2020. Change of sea reclamation and the sea-use management policy system in China. Marine Policy, 115: 103861 [DOI: 10.1016/j.marpol.2020.103861http://dx.doi.org/10.1016/j.marpol.2020.103861]
Li H Y, Jia M M, Zhang R, Ren Y X and Wen X. 2019. Incorporating the plant phenological trajectory into mangrove species mapping with dense time series Sentinel-2 imagery and the Google Earth Engine platform. Remote Sensing, 11(21): 2479 [DOI: 10.3390/rs11212479http://dx.doi.org/10.3390/rs11212479]
Li J, Gao S and Li Y. 2006. Spatial and temporal variations in salt-marsh vegetation in Wanggang area, Jiangsu coast, based upon TM imagery analysis. Marine Sciences, 30(5): 52-57
李婧, 高抒, 李炎. 2006. 江苏海岸王港地区盐沼植被变化的tm图像分析. 海洋科学, 30(5): 52-57 [DOI: 10.3969/j.issn.1000-3096.2006.05.011http://dx.doi.org/10.3969/j.issn.1000-3096.2006.05.011]
Li J, Gao S and Wang Y P. 2010. Invading cord grass vegetation changes analyzed from Landsat-TM imageries: a case study from the Wanggang area, Jiangsu coast, eastern China. Acta Oceanologica Sinica, 29(3): 26-37 [DOI: 10.1007/s13131-010-0034-yhttp://dx.doi.org/10.1007/s13131-010-0034-y]
Li J L. 2006. Research on the seasonal dynamics of zonal vegetation in the coastal area using MODIS-NDVI/EVI——a case study of Spartina alterniflora marsh on the Jiangsu coast. Marine Science Bulletin, 25(6): 91-96
李加林. 2006. 基于MODIS的沿海带状植被NDVI/EVI季节变化研究——以江苏沿海互花米草盐沼为例. 海洋通报, 25(6): 91-96 [DOI: 10.3969/j.issn.1001-6392.2006.06.013http://dx.doi.org/10.3969/j.issn.1001-6392.2006.06.013]
Li Q Q, Lu Y, Hu S B, Hu Z W, Li H Z, Liu P, Shi T Z, Wang C S, Wang J J and Wu G F. 2016. Review of remotely sensed geo-environmental monitoring of coastal zones. Journal of Remote Sensing, 20(5): 1216-1229
李清泉, 卢艺, 胡水波, 胡忠文, 李洪忠, 刘鹏, 石铁柱, 汪驰升, 王俊杰, 邬国锋. 2016. 海岸带地理环境遥感监测综述. 遥感学报, 20(5): 1216-1229 [DOI: 10.11834/jrs.20166168http://dx.doi.org/10.11834/jrs.20166168]
Li X, Yu M J, Shen J H, Hu R Y and Li M H. 2007. Effects of soil Na content on plant diversity and dominance in the wetland of the Hangzhou Bay. Acta Ecologica Sinica, 27(11): 4603-4611
李侠, 于明坚, 慎佳泓, 胡仁勇, 李铭红. 2007. 杭州湾滩涂Na元素含量对植物多样性和优势度的影响. 生态学报, 27(11): 4603-4611 [DOI: 10.3321/j.issn:1000-0933.2007.11.028http://dx.doi.org/10.3321/j.issn:1000-0933.2007.11.028]
Li Z, Guo X D, Gu C, Yao K, Lyu C Y and Zhang L L. 2016. Spectral absorption characteristic parameters in inversing FAPAR of natural grassland. Journal of Remote Sensing, 20(2): 290-302
李喆, 郭旭东, 古春, 姚阔, 吕春艳, 张路路. 2016. 高光谱吸收特征参数反演草地光合有效辐射吸收率. 遥感学报, 20(2): 290-302 [DOI: 10.11834/jrs.20165174http://dx.doi.org/10.11834/jrs.20165174]
Liang T C, Liang S L, Zou L Q, Sun L, Li B, Lin H, He T and Tian F. 2022. Estimation of aerosol optical depth at 30 m resolution using landsat imagery and machine learning. Remote Sensing, 14(5): 1053 [DOI: 10.3390/rs14051053http://dx.doi.org/10.3390/rs14051053]
Liu L, Xiao X M, Qin Y W, Wang J, Xu X L, Hu Y M and Qiao Z. 2020. Mapping cropping intensity in China using time series Landsat and Sentinel-2 images and Google Earth Engine. Remote Sensing of Environment, 239: 111624 [DOI: 10.1016/j.rse.2019.111624http://dx.doi.org/10.1016/j.rse.2019.111624]
Liu R Q, Li J L, Sun C, Sun W W, Cao L D and Tian P. 2021. Classification of Yancheng coastal wetland vegetation based on vegetation phenological characteristics derived from Sentinel-2 time-series. Acta Geographica Sinica, 76(7): 1680-1692
刘瑞清, 李加林, 孙超, 孙伟伟, 曹罗丹, 田鹏. 2021. 基于Sentinel-2遥感时间序列植被物候特征的盐城滨海湿地植被分类. 地理学报, 76(7): 1680-1692 [DOI: 10.11821/dlxb202107008http://dx.doi.org/10.11821/dlxb202107008]
Liu Y X, Li M C, Mao L, Cheng L and Chen K F. 2013. Seasonal pattern of tidal-flat topography along the Jiangsu middle coast, China, using HJ-1 optical images. Wetlands, 33(5): 871-886 [DOI: 10.1007/s13157-013-0445-6http://dx.doi.org/10.1007/s13157-013-0445-6]
Mao D H, Wang Z M, Luo L, Ren C Y and Jia M M. 2016. Monitoring the evolution of wetland ecosystem pattern in northeast China from 1990 to 2013 based on remote sensing. Journal of Natural Resources, 31(8): 1253-1263
毛德华, 王宗明, 罗玲, 任春颖, 贾明明. 2016. 1990-2013年中国东北地区湿地生态系统格局演变遥感监测分析. 自然资源学报, 31(8): 1253-1263 [DOI: 10.11849/zrzyxb.20151005http://dx.doi.org/10.11849/zrzyxb.20151005]
Minello T J, Rozas L P and Baker R. 2012. Geographic variability in salt marsh flooding patterns may affect nursery value for fishery species. Estuaries and Coasts, 35(2): 501-514 [DOI: 10.1007/s12237-011-9463-xhttp://dx.doi.org/10.1007/s12237-011-9463-x]
Niu Z G, Zhang H Y, Wang X W, Yao W B, Zhou D M, Zhao K Y, Zhao H, Li N N, Huang H B, Li C C, Yang J, Liu C X, Liu S, Wang L, Li Z, Yang Z Z, Qiao F, Zheng Y M, Chen Y L, Sheng Y W, Gao X H, Zhu W H, Wang W Q, Wang H, Weng Y L, Zhuang D F, Liu J Y, Luo Z C, Cheng X, Guo Z Q and Gong P. 2012. Mapping wetland changes in China between 1978 and 2008. Chinese Science Bulletin, 57(22): 2813-2823
牛振国, 张海英, 王显威, 姚文博, 周德民, 赵魁义, 赵惠, 李娜娜, 黄华兵, 李丛丛, 杨军, 柳彩霞, 刘爽, 王琳, 李展, 杨镇钟, 乔飞, 郑姚闽, 陈炎磊, 盛永伟, 高小红, 朱卫红, 王文卿, 王红, 翁永玲, 庄大方, 刘纪远, 罗志才, 程晓, 郭子琪, 宫鹏. 2012. 1978~2008年中国湿地类型变化. 科学通报, 57(16): 1400-1411 [DOI: 10.1007/s11434-012-5093-3http://dx.doi.org/10.1007/s11434-012-5093-3]
Portnoy J W and Valiela I. 1997. Short-term effects of salinity reduction and drainage on salt-marsh biogeochemical cycling and Spartina (cordgrass) production. Estuaries, 20(3): 569-578 [DOI: 10.2307/1352615http://dx.doi.org/10.2307/1352615]
Reiche J, Hamunyela E, Verbesselt J, Hoekman D and Herold M. 2018. Improving near-real time deforestation monitoring in tropical dry forests by combining dense Sentinel-1 time series with Landsat and ALOS-2 PALSAR-2. Remote Sensing of Environment, 204: 147-161 [DOI: 10.1016/j.rse.2017.10.034http://dx.doi.org/10.1016/j.rse.2017.10.034]
Ren G B, Zhang J and Ma Y. 2015. Spectral discrimination and separable feature lookup table of typical vegetation species in Yellow River Delta wetland. Marine Environmental Science, 34(3): 420-426
任广波, 张杰, 马毅. 2015. 黄河三角洲典型植被地物光谱特征分析与可分性查找表. 海洋环境科学, 34(3): 420-426 [DOI: 10.13634/j.cnki.mes.2015.03.017http://dx.doi.org/10.13634/j.cnki.mes.2015.03.017]
Roy D P, Kovalskyy V, Zhang H K, Vermote E F, Yan L, Kumar S S and Egorov A. 2016. Characterization of Landsat-7 to Landsat-8 reflective wavelength and normalized difference vegetation index continuity. Remote sensing of Environment, 185: 57-70 [DOI: 10.1016/j.rse.2015.12.024http://dx.doi.org/10.1016/j.rse.2015.12.024]
Rozema J, Bijwaard P, Prast G and Broekman R. 1985. Ecophysiological adaptations of coastal halophytes from foredunes and salt marshes. Vegetatio, 62(1): 499-521 [DOI: 10.1007/BF00044777http://dx.doi.org/10.1007/BF00044777]
Song J and Yu C B. 2021. Missing value imputation using XGboost for label-free mass spectrometry-based proteomics data. BioRxiv [DOI: 10.1101/2021.04.08.438945http://dx.doi.org/10.1101/2021.04.08.438945]
Sun C, Li J L, Cao L D, Liu Y C, Jin S and Zhao B X. 2020. Evaluation of vegetation Index-Based curve fitting models for accurate classification of salt marsh vegetation using sentinel-2 Time-Series. Sensors, 20(19): 5551 [DOI: 10.3390/s20195551http://dx.doi.org/10.3390/s20195551]
Sun C, Li J L, Liu Y X, Liu Y C and Liu R Q. 2021. Plant species classification in salt marshes using phenological parameters derived from Sentinel-2 pixel-differential time-series. Remote Sensing of Environment, 256: 112320 [DOI: 10.1016/j.rse.2021.112320http://dx.doi.org/10.1016/j.rse.2021.112320]
Sun C, Liu Y X, Li M C, Zhao S S and Zhou M X. 2015. Spatiotemporal evolution of salt marsh and influential analysis of reclamation of Jiangsu middle coast in recent 25 years. Journal of Natural Resources, 30(9): 1486-1498
孙超, 刘永学, 李满春, 赵赛帅, 周旻曦. 2015. 近25a来江苏中部沿海盐沼分布时空演变及围垦影响分析. 自然资源学报, 30(9): 1486-1498 [DOI: 10.11849/zrzyxb.2015.09.006http://dx.doi.org/10.11849/zrzyxb.2015.09.006]
Sun C, Liu Y X, Zhao S S, Zhou M X, Yang Y H and Li F X. 2016. Classification mapping and species identification of salt marshes based on a short-time interval NDVI time-series from HJ-1 optical imagery. International Journal of Applied Earth Observation and Geoinformation, 45: 27-41 [DOI: 10.1016/j.jag.2015.10.008http://dx.doi.org/10.1016/j.jag.2015.10.008]
Sun X B and Liu H Y. 2010. Optimization of wetland landscape patterns based on ecological function evaluation: a case study on the coastal wetlands of Yancheng, Jiangsu Province. Acta Ecologica Sinica, 30(5): 1157-1166
孙贤斌, 刘红玉. 2010. 基于生态功能评价的湿地景观格局优化及其效应——以江苏盐城海滨湿地为例. 生态学报, 30(5): 1157-1166
Tian B, Zhou Y X, Zhang L Q, Ma Z J, Yang B and Tang C D. 2008. A GIS and remote sensing-based analysis of migratory bird habitat suitability for Chongming Dongtan Nature Reserve, Shanghai. Acta Ecologica Sinica, 28(7): 3049-3059
田波, 周云轩, 张利权, 马志军, 杨波, 汤臣栋. 2008. 遥感与GIS支持下的崇明东滩迁徙鸟类生境适宜性分析. 生态学报, 28(7): 3049-3059
Tian Y C, Zhang Q, Huang H, Huang Y J, Tao J, Zhou G Q, Zhang Y L, Yang Y W and Lin J L. 2022. Aboveground biomass of typical invasive mangroves and its distribution patterns using UAV-LiDAR data in a subtropical estuary: Maoling River estuary, Guangxi, China. Ecological Indicators, 136: 108694
Van Beijma S, Comber A and Lamb A. 2014. Random forest classification of salt marsh vegetation habitats using quad-polarimetric airborne SAR, elevation and optical RS data. Remote Sensing of Environment, 149: 118-129 [DOI: 10.1016/j.rse.2014.04.010http://dx.doi.org/10.1016/j.rse.2014.04.010]
Wang Z M, Song K S, Liu D W, Zhang B, Zhang S Q, Li F, Ren C Y, Jin C, Yang T and Zhang C H. 2009. Process of Land Conversion from Marsh into Cropland in the Sanjiang Plain during 1954-2005. Wetland Science, 7(3): 208-217
王宗明, 宋开山, 刘殿伟, 张柏, 张树清, 李方, 任春颖, 金翠, 杨婷, 张春华. 2009. 1954～2005年三江平原沼泽湿地农田化过程研究. 湿地科学, 7(3): 208-217 [DOI: 10.13248/j.cnki.wetlandsci.2009.03.013http://dx.doi.org/10.13248/j.cnki.wetlandsci.2009.03.013]
Xi Y, Zhuang X T, Wang X M, Nie R H and Zhao G S. 2018. A research and application based on gradient boosting decision tree//15th International Conference on Web Information Systems and Applications. Taiyuan: Springer: 15-26 [DOI: 10.1007/978-3-030-02934-0_2http://dx.doi.org/10.1007/978-3-030-02934-0_2]
Xiao C W, Li P, Feng Z M and Wu X Y. 2018. Spatio-temporal differences in cloud cover of Landsat-8 OLI observations across China during 2013-2016. Journal of Geographical Sciences, 28(4): 429-444 [DOI: 10.1007/s11442-018-1482-0http://dx.doi.org/10.1007/s11442-018-1482-0]
Zhang J, Li J R, Yang L and Li X. 2019. Current situation, problems and management countermeasures of coastal wetlands in China. Environment and Sustainable Development, 44(5): 127-129
张健, 李佳芮, 杨璐, 李潇. 2019. 中国滨海湿地现状和问题及管理对策建议. 环境与可持续发展, 44(5): 127-129 [DOI: 10.19758/j.cnki.issn1673-288x.201905127http://dx.doi.org/10.19758/j.cnki.issn1673-288x.201905127]
Zhang X M, Yan C, Gao C, Malin B A and Chen Y. 2020. Predicting missing values in medical data via XGBoost regression. Journal of Healthcare Informatics Research, 4(4): 383-394 [DOI: 10.1007/s41666-020-00077-1http://dx.doi.org/10.1007/s41666-020-00077-1]
Zhao B, Yan Y E, Guo H Q, He M M, Gu Y J and Li B. 2009. Monitoring rapid vegetation succession in estuarine wetland using time series MODIS-based indicators: an application in the Yangtze River Delta area. Ecological Indicators, 9(2): 346-356 [DOI: 10.1016/j.ecolind.2008.05.009http://dx.doi.org/10.1016/j.ecolind.2008.05.009]
Zhu Z. 2017. Change detection using landsat time series: a review of frequencies, preprocessing, algorithms, and applications. ISPRS Journal of Photogrammetry and Remote Sensing, 130: 370-384 [DOI: 10.1016/j.isprsjprs.2017.06.013http://dx.doi.org/10.1016/j.isprsjprs.2017.06.013]
Zhu Z and Woodcock C E. 2012. Object-based cloud and cloud shadow detection in Landsat imagery. Remote Sensing of Environment, 118: 83-94 [DOI: 10.1016/j.rse.2011.10.028http://dx.doi.org/10.1016/j.rse.2011.10.028]
Zhu Z, Wulder M A, Roy D P, Woodcock C E, Hansen M C, Radeloff V C, Healey S P, Schaaf C, Hostert P, Strobl P, Pekel J F, Lymburner L, Pahlevan N and Scambos T A. 2019. Benefits of the free and open Landsat data policy. Remote Sensing of Environment, 224: 382-385 [DOI: 10.1016/j.rse.2019.02.016http://dx.doi.org/10.1016/j.rse.2019.02.016]
Zuo P, Li Y, Zhao S H, Zhou X, Teng H F and Chen H. 2012. Landscape changes of Jiangsu Yancheng coastal wetlands and their driving forces since 1976. Acta Oceanologica Sinica, 34(1): 101-108
左平, 李云, 赵书河, 周鑫, 滕厚锋, 陈浩. 2012. 1976年以来江苏盐城滨海湿地景观变化及驱动力分析. 海洋学报, 34(1): 101-108
Zuo P, Zhao S H, Liu C A, Wang C H and Liang Y B. 2012. Distribution of Spartina spp. along China’s coast. Ecological Engineering, 40: 160-166 [DOI: 10.1016/j.ecoleng.2011.12.014http://dx.doi.org/10.1016/j.ecoleng.2011.12.014]
相关文章
相关作者
相关机构
京公网安备11010802024621