Classification of the Yellow River Delta wetland landscape based on ZY-1 02D hyperspectral imagery

HAN Yue; KE Yinghai; WANG Zhanpeng; LIANG Deyin; ZHOU Demin

doi:10.11834/jrs.20211071

Wetland fine classification method | Views : 0 下载量: 440 CSCD: 0 更多指标

R-PDF
PDF
Export
Share
Collection
Album

Classification of the Yellow River Delta wetland landscape based on ZY-1 02D hyperspectral imagery
Vol. 27, Issue 6, Pages: 1387-1399(2023)
Published： 07 June 2023 ，
DOI： 10.11834/jrs.20211071

扫描看全文

韩月，柯樱海，王展鹏，梁德印，周德民.2023.资源一号02D卫星高光谱数据黄河三角洲湿地景观分类.遥感学报，27（6）： 1387-1399

Han Y，Ke Y H，Wang Z P，Liang D Y and Zhou D M. 2023. Classification of the Yellow River Delta wetland landscape based on ZY-1 02D hyperspectral imagery. National Remote Sensing Bulletin， 27（6）：1387-1399
韩月，柯樱海，王展鹏，梁德印，周德民.2023.资源一号02D卫星高光谱数据黄河三角洲湿地景观分类.遥感学报，27（6）： 1387-1399 DOI： 10.11834/jrs.20211071.

Han Y，Ke Y H，Wang Z P，Liang D Y and Zhou D M. 2023. Classification of the Yellow River Delta wetland landscape based on ZY-1 02D hyperspectral imagery. National Remote Sensing Bulletin， 27（6）：1387-1399 DOI： 10.11834/jrs.20211071.

摘要

资源一号02D卫星（ZY-1 02D）于2019年成功发射，2020年10月正式投入使用，是中国自主建造并成功运行的首颗民用高光谱业务卫星，具有广泛的应用前景。本研究以黄河三角洲湿地为研究区，以ZY-1 02D高光谱（AHSI）影像为数据源，结合无人机和地面调查数据，开展湿地景观分类研究。首先通过ZY-1 02D AHSI获取地物反射率波谱曲线，分析不同地物波谱曲线的差异，作为地物识别和分类的依据；充分考虑研究区植被覆盖度的差异，结合无人机影像制定研究区7类基本地物和9类精细地物两种湿地景观分类体系；利用随机森林算法进行分类，并引入Tree SHAP方法进行波段重要性排序和选择；探究影响ZY-1 02D AHSI分类的重要波段，选取与Landsat 8 OLI多光谱波段相重叠的波段进行分类，并与Landsat 8 OLI分类结果进行比较。结果表明：（1）ZY-1 02D AHSI数据能够较好地反映不同地物类型光谱曲线的差异；（2）对于两种分类体系，仅用前40个重要波段的总体分类精度达到最高，7类基本地物分类和9类精细地物分类的分类精度分别为92.18%和90.76%，这40个波段大多位于可见光、近红外波段；（3）对于两种分类体系，分别选取与Landsat 8 OLI多光谱波段重叠的24个和29个波段进行分类，分类精度达到90.01%和89.76%，均明显高于Landsat 8 OLI的分类精度；（4）蓝、绿波段对于识别高、低密度互花米草和芦苇较为重要，短波红外波段对于芦苇的识别较为重要，红波段对于识别高、低密度碱蓬较为重要。ZY-1 02D AHSI数据的波谱范围较窄，波谱连续，能够较好地体现地物光谱曲线的细微变化，在区分不同地物以及植被覆盖度差异上具有明显优势。本研究有利于及时有效地监测黄河三角洲湿地资源现状，为ZY-1 02D高光谱数据在湿地生态监测应用提供科学参考依据。

Abstract

The ZY-1 02D satellite was successfully launched in 2019 and officially used in October 2020. It is the first civil hyperspectral satellite independently developed and operated by China and has a wide application prospect. Taking the Yellow River Delta wetland as study area

this work investigated the performance of ZY-1 02D Advanced Hyperspectral Instrument (AHSI) images in wetland landscape classification. First

the ZY-1 02D AHSI spectral curves of typical land cover types in the study area were evaluated. Second

two levels of wetland landscape classification systems

including seven basic classes and nine refined classes considering the difference of vegetation coverage

were developed with the assistance of field surveys and unmanned aerial vehicle images. Then

the random forest algorithm was used for classification

and the tree SHAP method was introduced to sort and select important bands. The most important ZY-1 02D AHSI bands overlapping with Landsat 8 OLI bands were selected for classification

and the classification results were compared. Our results showed the following: (1) ZY-1 02D AHSI data can represent the differences of spectral features of different landscape types. (2) For the two classification systems

the first 40 important bands achieved the highest overall classification accuracy. The classification accuracy of seven basic classes and nine refined classes were 92.18% and 90.76%

respectively. Most of the important bands were visible and near-infrared bands. (3) For the two classification systems

a total of 24 and 29 bands overlapping with Landsat 8 OLI multispectral bands were selected for classification. The classification accuracy reached 90.01% and 89.76%

which were remarkably higher than that of Landsat 8 OLI. (4) Blue and green bands are important for the identification of high- and low-density

Spartina alterniflora

and

Phragmites australis

; blue

green

and short-wave infrared bands are important for

Phragmites australis

identification

and the red band is important for the identification of high- and low-density

Suaeda salsa

. Results showed that ZY-1 02D AHSI data have advantages in distinguishing different landscape features and differences in vegetation coverage. Our study is expected to provide scientific basis for the application of ZY-1 02D hyperspectral data in wetland ecological resources.

关键词

ZY-1 02DAHSI影像高光谱数据植被覆盖度随机森林Tree SHAP

Keywords

ZY-1 02DAHSI satellite imagehyperspectral datavegetation coveragerandom forestTree SHAP

references

Chen B Q, Xiao X M, Li X P, Pan L H, Doughty R, Ma J, Dong J W, Qin Y W, Zhao B, Wu Z X, Sun R, Lan G Y, Xie G S, Clinton N and Giri C. 2017. A mangrove forest map of China in 2015: analysis of time series Landsat 7/8 and Sentinel-1A imagery in Google Earth Engine cloud computing platform. ISPRS Journal of Photogrammetry and Remote Sensing, 131: 104-120. [DOI: 10.1016/j.isprsjprs.2017.07.011http://dx.doi.org/10.1016/j.isprsjprs.2017.07.011]

Cheng X Y, Cai Z Y, Li J, Wen M X, Wang Y M and Zeng D. 2021. A spatial-spectral clustering-based algorithm for endmember extraction and hyperspectral unmixing. International Journal of Remote Sensing, 42(5): 1948-1972. [DOI: 10.1080/01431161.2020.1849851http://dx.doi.org/10.1080/01431161.2020.1849851]

Du P J, Xia J S, Xue Z H, Tan K, Su H J and Bao R. 2016. Review of hyperspectral remote sensing image classification. Journal of Remote Sensing, 20(2): 236-256

杜培军, 夏俊士, 薛朝辉, 谭琨, 苏红军, 鲍蕊. 2016. 高光谱遥感影像分类研究进展. 遥感学报, 20(2): 236-256 [DOI: 10.11834/jrs.20165022http://dx.doi.org/10.11834/jrs.20165022]

Gong N, Niu Z G, Qi W and Zhang H Y. 2016. Driving forces of wetland change in China. Journal of Remote Sensing, 20(2): 172-183

宫宁, 牛振国, 齐伟, 张海英. 2016. 中国湿地变化的驱动力分析. 遥感学报, 20(2): 172-183 [DOI: 10.11834/jrs.20164210http://dx.doi.org/10.11834/jrs.20164210]

Hughes G. 1968. On the mean accuracy of statistical pattern recognizers. IEEE Transactions on Information Theory, 14(1): 55-63 [DOI: 10.1109/TIT.1968.1054102http://dx.doi.org/10.1109/TIT.1968.1054102]

Jiao L L, Sun W W, Yang G, Ren G B and Liu Y N. 2019. A hierarchical classification framework of satellite multispectral/hyperspectral images for mapping coastal wetlands. Remote Sensing, 11(19): 2238 [DOI: 10.3390/rs11192238http://dx.doi.org/10.3390/rs11192238]

Li C, Chen G, Chu W Q and Hu H H. 2021. Research on main influencing factors of bursting of urban water supply network based on improved SHAP. Bulletin of Science and Technology, 37(1): 79-84

李超, 陈功, 储文强, 胡鸿昊. 2021. 基于改进SHAP的城市供水管网爆管主影响因素研究. 科技通报, 37(1): 79-84 [DOI: 10.13774/j.cnki.kjtb.2021.01.014http://dx.doi.org/10.13774/j.cnki.kjtb.2021.01.014]

Li P, Li D H, Li Z H and Wang H J. 2019. Wetland classification through integration of GF-3 SAR and sentinel-2B multispectral data over the Yellow River Delta. Geomatics and Information Science of Wuhan University, 44(11): 1641-1649

李鹏, 黎达辉, 李振洪, 王厚杰. 2019. 黄河三角洲地区GF-3雷达数据与Sentinel-2多光谱数据湿地协同分类研究. 武汉大学学报(信息科学版), 44(11): 1641-1649 [DOI: 10.13203/j.whugis20180258http://dx.doi.org/10.13203/j.whugis20180258]

Li Y, Li Y R, Wang G H and Shi X. 2020. A hyperspectral image classification algorithm based on the weighted exponential function model. Journal of Geo-Information Science, 22(8): 1642-1653

李玉, 李奕燃, 王光辉, 石雪. 2020. 基于加权指数函数模型的高光谱图像分类方法. 地球信息科学学报, 22(8): 1642-1653 [DOI: 10.12082/dqxxkx.2020.190383http://dx.doi.org/10.12082/dqxxkx.2020.190383]

Li Y T, Du Z Y, Wang X, Yang Q S, Chen Z Q, Sun Y Y and Liu D X. 2019. Evaluation of wetland ecosystem services in Yellow River delta nature reserve. Marine Environmental Science, 38(5): 761-768

李永涛, 杜振宇, 王霞, 杨庆山, 陈占强, 孙燕燕, 刘德玺. 2019. 黄河三角洲自然保护区湿地生态服务功能价值评估. 海洋环境科学, 38(5): 761-768 [DOI: 10.13634/j.cnki.mes.2019.05.017http://dx.doi.org/10.13634/j.cnki.mes.2019.05.017]

Liu S and Xing G L. 2020. Multi-dimensional convolutional network collaborative unmixing method for hyperspectral image mixed pixels. Acta Geodaetica et Cartographica Sinica, 49(12): 1600-1608

刘帅, 邢光龙. 2020. 高光谱图像混合像元多维卷积网络协同分解法. 测绘学报, 49(12): 1600-1608 [DOI: 10.11947/j.AGCS.2020.20190461http://dx.doi.org/10.11947/j.AGCS.2020.20190461]

Lundberg S M, Erion G G and Lee S I. 2018. Consistent individualized feature attribution for tree ensembles. arXiv: 1802.03888.

Lundberg S M and Lee S I. 2017. A unified approach to interpreting model predictions//Proceedings of the 31st International Conference on Neural Information Processing Systems. Long Beach: ACM: 4768-4777 [DOI: 10.5555/3295222.3295230http://dx.doi.org/10.5555/3295222.3295230]

Rodríguez-Pérez R and Bajorath J. 2020. Interpretation of compound activity predictions from complex machine learning models using local approximations and Shapley values. Journal of Medicinal Chemistry, 63(16): 8761-8777 [DOI: 10.1021/acs.jmedchem.9b01101http://dx.doi.org/10.1021/acs.jmedchem.9b01101]

Slagter B, Tsendbazar N E, Vollrath A and Reiche J. 2020. Mapping wetland characteristics using temporally dense Sentinel-1 and Sentinel-2 data: a case study in the St. Lucia wetlands, South Africa. International Journal of Applied Earth Observation and Geoinformation, 86: 102009 [DOI: 10.1016/j.jag.2019.102009http://dx.doi.org/10.1016/j.jag.2019.102009]

Sun Z G, Song H L and Hu X Y. 2017. Response of germination and seeding growth of Suaeda salsa seeds from tidal marshes in the Yellow River estuary to N/P ratio of soil. Wetland Science, 15(1): 10-19

孙志高, 宋红丽, 胡星云. 2017. 黄河口潮滩碱蓬种子萌发与幼苗生长对土壤中氮磷比的响应. 湿地科学, 15(1): 10-19 [DOI: 10.13248/j.cnki.wetlandsci.2017.01.002http://dx.doi.org/10.13248/j.cnki.wetlandsci.2017.01.002]

Wan L M, Lin Y Y, Zhang H S, Wang F, Liu M F and Lin H. 2020. GF-5 hyperspectral data for species mapping of mangrove in Mai Po, Hong Kong. Remote Sensing, 12(4): 656 [DOI: 10.3390/rs12040656http://dx.doi.org/10.3390/rs12040656]

Wang X P. 2014. Study on the Yellow River Delta Wetland Typical Vegetation Using Hyperspectral Remote Sensing. Dalian: Dalian Maritime University

王霄鹏. 2014. 黄河三角洲湿地典型植被高光谱遥感研究. 大连: 大连海事大学

Wang X P, Zhang J, Ma Y and Ren G B. 2014. Comparison of coastal wetland classification accuracy using fused images of different-polarization SAR and TM images. Journal of Marine Sciences, 32(1): 40-46

王霄鹏, 张杰, 马毅, 任广波. 2014. 不同极化方式SAR与TM融合影像滨海湿地分类精度比较. 海洋学研究, 32(1): 40-46 [DOI: 10.3969/j.issn.1001-909X.2014.01.005http://dx.doi.org/10.3969/j.issn.1001-909X.2014.01.005]

Xu Z T. 2020. Information Extraction and Dynamic Change Analysis of Yellow River Delta Wetland Based on Landsat Data. Qingdao: Qingdao University

徐振田. 2020. 基于Landsat数据的黄河三角洲湿地信息提取及动态变化分析. 青岛: 青岛大学 [DOI: 10.27262/d.cnki.gqdau.2020.002244http://dx.doi.org/10.27262/d.cnki.gqdau.2020.002244]

Zhang L, Gong Z N, Wang Q W, Jin D D and Wang X. 2019. Wetland mapping of Yellow River Delta wetlands based on multi-feature optimization of Sentinel-2 images. Journal of Remote Sensing, 23(2): 313-326

张磊, 宫兆宁, 王启为, 金点点, 汪星. 2019. Sentinel-2影像多特征优选的黄河三角洲湿地信息提取. 遥感学报, 23(2): 313-326 [DOI: 10.11834/jrs.20198083http://dx.doi.org/10.11834/jrs.20198083]

Zhang L P and Zhang L F. 2005. Hyperspectral Remote Sensing. Wuhan: Wuhan University Press: 1-8

张良培, 张立福. 2005. 高光谱遥感. 武汉: 武汉大学出版社: 1-8

Zhang Y C, Na X D and Zang S Y. 2018. Wetland high precision classification based on the HJ-1A hyperspectral image. Progress in Geography 37(12): 1705-1712

张雅春, 那晓东, 臧淑英. 2018. 基于HJ-1A高光谱影像的湿地精细分类. 地理科学进展, 37(12): 1705-1712 [DOI: 10.18306/dlkxjz.2018.12.012http://dx.doi.org/10.18306/dlkxjz.2018.12.012]

Alert me when the article has been cited

提交

Improving the quality of remotely sensed precipitation product from GPM satellites by using a spatial random forest

Remote sensing extraction of paddy rice in Northeast China from GF-6 images by combining feature optimization and random forest

Estimation of fractional woody and herbaceous vegetation cover in Temperate Sparse Forest Grassland using fusion of UAV and Satellite imagery

Refined wetland classification of international wetland cities based on the random forest algorithm and knowledge-driven rules： A case study of Changde city， China

UAV hyperspectral inversion of Suaeda Salsa leaf area index in coastal wetlands combined with multimodal data