{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,29]],"date-time":"2026-06-29T14:21:33Z","timestamp":1782742893669,"version":"3.54.5"},"reference-count":57,"publisher":"MDPI AG","issue":"15","license":[{"start":{"date-parts":[[2022,8,5]],"date-time":"2022-08-05T00:00:00Z","timestamp":1659657600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["42101412"],"award-info":[{"award-number":["42101412"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>The main feature of grassland degradation is the change in the vegetation community structure. Hyperspectral-based grassland community identification is the basis and a prerequisite for large-area high-precision grassland degradation monitoring and management. To obtain the distribution pattern of grassland communities in Xilinhot, Inner Mongolia Autonomous Region, China, we propose a systematic classification method (SCM) for hyperspectral grassland community identification using China\u2019s ZiYuan 1-02D (ZY1-02D) satellite. First, the sample label data were selected from the field-collected samples, vegetation map data, and function zoning data for the Nature Reserve. Second, the spatial features of the images were extracted using extended morphological profiles (EMPs) based on the reduced dimensionality of principal component analysis (PCA). Then, they were input into the random forest (RF) classifier to obtain the preclassification results for grassland communities. Finally, to reduce the influence of salt-and-pepper noise, the label similarity probability filter (LSPF) method was used for postclassification processing, and the RF was again used to obtain the final classification results. The results showed that, compared with the other seven (e.g., SVM, RF, 3D-CNN) methods, the SCM obtained the optimal classification results with an overall classification accuracy (OCA) of 94.56%. In addition, the mapping results of the SCM showed its ability to accurately identify various ground objects in large-scale grassland community scenes.<\/jats:p>","DOI":"10.3390\/rs14153751","type":"journal-article","created":{"date-parts":[[2022,8,9]],"date-time":"2022-08-09T04:16:55Z","timestamp":1660018615000},"page":"3751","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":16,"title":["A Systematic Classification Method for Grassland Community Division Using China\u2019s ZY1-02D Hyperspectral Observations"],"prefix":"10.3390","volume":"14","author":[{"given":"Dandan","family":"Wei","sequence":"first","affiliation":[{"name":"Land Satellite Remote Sensing Application Center, Ministry of Natural Resources, Beijing 100048, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kai","family":"Liu","sequence":"additional","affiliation":[{"name":"Land Satellite Remote Sensing Application Center, Ministry of Natural Resources, Beijing 100048, China"},{"name":"Department of Geography and Spatial Information Techniques, Ningbo University, Ningbo 315211, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chenchao","family":"Xiao","sequence":"additional","affiliation":[{"name":"Land Satellite Remote Sensing Application Center, Ministry of Natural Resources, Beijing 100048, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Weiwei","family":"Sun","sequence":"additional","affiliation":[{"name":"Department of Geography and Spatial Information Techniques, Ningbo University, Ningbo 315211, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0536-8176","authenticated-orcid":false,"given":"Weiwei","family":"Liu","sequence":"additional","affiliation":[{"name":"Department of Geography and Spatial Information Techniques, Ningbo University, Ningbo 315211, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lidong","family":"Liu","sequence":"additional","affiliation":[{"name":"Department of Geography and Spatial Information Techniques, Ningbo University, Ningbo 315211, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xizhi","family":"Huang","sequence":"additional","affiliation":[{"name":"Land Satellite Remote Sensing Application Center, Ministry of Natural Resources, Beijing 100048, China"},{"name":"Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chunyong","family":"Feng","sequence":"additional","affiliation":[{"name":"Land Satellite Remote Sensing Application Center, Ministry of Natural Resources, Beijing 100048, China"},{"name":"Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,8,5]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"86","DOI":"10.1016\/j.actao.2013.12.006","article-title":"Dynamic of grassland vegetation degradation and its quantitative assessment in the northwest China","volume":"55","author":"Zhou","year":"2014","journal-title":"Acta Oecologica"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"27","DOI":"10.1016\/j.scitotenv.2019.06.503","article-title":"Grassland dynamics in responses to climate variation and human activities in China from 2000 to 2013","volume":"690","author":"Liu","year":"2019","journal-title":"Sci. Total Environ."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"84","DOI":"10.1016\/j.rama.2015.09.003","article-title":"Grassland carbon sequestration ability in China: A new perspective from terrestrial aridity zones","volume":"69","author":"Chen","year":"2016","journal-title":"Rangel. Ecol. Manag."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"20190327","DOI":"10.1098\/rsta.2019.0327","article-title":"Effects of ozone on agriculture, forests and grasslands","volume":"378","author":"Emberson","year":"2020","journal-title":"Philos. Trans. R. Soc. A"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"303","DOI":"10.1016\/j.ecolind.2017.08.019","article-title":"Grassland degradation remote sensing monitoring and driving factors quantitative assessment in China from 1982 to 2010","volume":"83","author":"Zhou","year":"2017","journal-title":"Ecol. Indic."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"4199","DOI":"10.1007\/s10661-014-4199-2","article-title":"Differentiating climate-and human-induced drivers of grassland degradation in the Liao River Basin, China","volume":"187","author":"He","year":"2015","journal-title":"Environ. Monit. Assess."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"2888","DOI":"10.1038\/s41598-018-21089-3","article-title":"Grassland dynamics in response to climate change and human activities in Xinjiang from 2000 to 2014","volume":"8","author":"Zhang","year":"2018","journal-title":"Sci. Rep."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"9079","DOI":"10.1038\/s41598-018-27150-5","article-title":"Grassland ecosystem responses to climate change and human activities within the Three-River Headwaters region of China","volume":"8","author":"Han","year":"2018","journal-title":"Sci. Rep."},{"key":"ref_9","first-page":"1979","article-title":"Ecological and environmental issues faced by a developing Tibet","volume":"46","author":"Yu","year":"2012","journal-title":"ACS Publ."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"110651","DOI":"10.1016\/j.jenvman.2020.110651","article-title":"Assessment of aquatic ecological health based on determination of biological community variability of fish and macroinvertebrates in the Weihe River Basin, China","volume":"267","author":"Wu","year":"2020","journal-title":"J. Environ. Manag."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"389","DOI":"10.1007\/BF00333714","article-title":"A global analysis of root distributions for terrestrial biomes","volume":"108","author":"Jackson","year":"1996","journal-title":"Oecologia"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"1465","DOI":"10.1098\/rstb.2004.1525","article-title":"Global climate and the distribution of plant biomes","volume":"359","author":"Woodward","year":"2004","journal-title":"Philos. Trans. R. Soc. Lond. Ser. B Biol. Sci."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"56","DOI":"10.1016\/j.isprsjprs.2012.03.006","article-title":"Discriminating indicator grass species for rangeland degradation assessment using hyperspectral data resampled to AISA Eagle resolution","volume":"70","author":"Mansour","year":"2012","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_14","first-page":"1","article-title":"Rangeland degradation in northern China and strategies for its prevention","volume":"30","author":"Li","year":"1997","journal-title":"Sci. Agric. Sin."},{"key":"ref_15","first-page":"29","article-title":"Current status and development of grassland monitoring in China","volume":"25","author":"Quangong","year":"2008","journal-title":"Pratacult. Sci."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"206","DOI":"10.1002\/qj.3161","article-title":"Probability of intense precipitation from polarimetric GNSS radio occultation observations","volume":"144","author":"Cardellach","year":"2018","journal-title":"Q. J. R. Meteorol. Soc."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"59801","DOI":"10.1109\/ACCESS.2020.2979219","article-title":"A novel marine oil spillage identification scheme based on convolution neural network feature extraction from fully polarimetric SAR imagery","volume":"8","author":"Song","year":"2020","journal-title":"IEEE Access"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"1758","DOI":"10.3390\/rs70201758","article-title":"Evaluation of satellite rainfall estimates for drought and flood monitoring in Mozambique","volume":"7","author":"Patricio","year":"2015","journal-title":"Remote Sens."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Tian, H., Wu, M., Wang, L., and Niu, Z. (2018). Mapping early, middle and late rice extent using sentinel-1A and Landsat-8 data in the poyang lake plain, China. Sensors, 18.","DOI":"10.3390\/s18010185"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Balogun, A.-L., Yekeen, S.T., Pradhan, B., and Althuwaynee, O.F. (2020). Spatio-temporal analysis of oil spill impact and recovery pattern of coastal vegetation and wetland using multispectral satellite landsat 8-OLI imagery and machine learning models. Remote Sens., 12.","DOI":"10.3390\/rs12071225"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Wang, X., Gao, X., Zhang, Y., Fei, X., Chen, Z., Wang, J., Zhang, Y., Lu, X., and Zhao, H. (2019). Land-cover classification of coastal wetlands using the RF algorithm for Worldview-2 and Landsat 8 images. Remote Sens., 11.","DOI":"10.3390\/rs11161927"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"238","DOI":"10.1016\/j.isprsjprs.2020.11.018","article-title":"Kernel low-rank representation with elastic net for China coastal wetland land cover classification using GF-5 hyperspectral imagery","volume":"171","author":"Su","year":"2021","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"1582","DOI":"10.1080\/01431161.2019.1673915","article-title":"The contribution of ALOS\/PALSAR-1 multi-temporal data to map permanently and temporarily flooded coastal wetlands","volume":"41","author":"Morandeira","year":"2020","journal-title":"Int. J. Remote Sens."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Shen, X., Wang, D., Mao, K., Anagnostou, E., and Hong, Y. (2019). Inundation extent mapping by synthetic aperture radar: A review. Remote Sens., 11.","DOI":"10.3390\/rs11070879"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"80","DOI":"10.1016\/j.compag.2017.11.027","article-title":"A novel approach for vegetation classification using UAV-based hyperspectral imaging","volume":"144","author":"Ishida","year":"2018","journal-title":"Comput. Electron. Agric."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"159","DOI":"10.1109\/MGRS.2019.2912563","article-title":"Deep learning for classification of hyperspectral data: A comparative review","volume":"7","author":"Audebert","year":"2019","journal-title":"IEEE Geosci. Remote Sens. Mag."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"181","DOI":"10.1016\/j.rse.2015.05.023","article-title":"Measuring freshwater aquatic ecosystems: The need for a hyperspectral global mapping satellite mission","volume":"167","author":"Hestir","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_28","first-page":"102572","article-title":"A simple and effective spectral-spatial method for mapping large-scale coastal wetlands using China ZY1-02D satellite hyperspectral images","volume":"104","author":"Sun","year":"2021","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"150","DOI":"10.1016\/j.cor.2012.05.015","article-title":"Supervised classification and mathematical optimization","volume":"40","author":"Carrizosa","year":"2013","journal-title":"Comput. Oper. Res."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Jiao, L., Sun, W., Yang, G., Ren, G., and Liu, Y. (2019). A hierarchical classification framework of satellite multispectral\/hyperspectral images for mapping coastal wetlands. Remote Sens., 11.","DOI":"10.3390\/rs11192238"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.ecss.2016.01.039","article-title":"Hyperspectral remote sensing of wild oyster reefs","volume":"172","author":"Rosa","year":"2016","journal-title":"Estuar. Coast. Shelf Sci."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"93","DOI":"10.1109\/LGRS.2005.857031","article-title":"Composite kernels for hyperspectral image classification","volume":"3","year":"2006","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"2200","DOI":"10.1109\/JSTARS.2014.2306956","article-title":"Joint Within-Class Collaborative Representation for Hyperspectral Image Classification","volume":"7","author":"Li","year":"2014","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"7718","DOI":"10.1109\/TGRS.2019.2915809","article-title":"Spectral\u2013spatial hyperspectral image classification using a multiscale conservative smoothing scheme and adaptive sparse representation","volume":"57","author":"Gao","year":"2019","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Cao, X., Xu, Z., and Meng, D. (2019). Spectral-Spatial Hyperspectral Image Classification via Robust Low-Rank Feature Extraction and Markov Random Field. Remote Sens., 11.","DOI":"10.3390\/rs11131565"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"2666","DOI":"10.1109\/TGRS.2013.2264508","article-title":"Spectral\u2013spatial hyperspectral image classification with edge-preserving filtering","volume":"52","author":"Kang","year":"2013","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"3173","DOI":"10.1109\/TGRS.2018.2794326","article-title":"Hyperspectral image classification with deep feature fusion network","volume":"56","author":"Song","year":"2018","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"492","DOI":"10.1109\/TBDATA.2019.2923243","article-title":"Beyond the patchwise classification: Spectral-spatial fully convolutional networks for hyperspectral image classification","volume":"6","author":"Xu","year":"2019","journal-title":"IEEE Trans. Big Data"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"7831","DOI":"10.1109\/TGRS.2020.3043267","article-title":"Attention-based adaptive spectral\u2013spatial kernel ResNet for hyperspectral image classification","volume":"59","author":"Roy","year":"2020","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"120","DOI":"10.1016\/j.isprsjprs.2017.11.021","article-title":"A new deep convolutional neural network for fast hyperspectral image classification","volume":"145","author":"Paoletti","year":"2018","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_41","first-page":"2","article-title":"Spatial-Spectral Classification of Hyperspectral Images Based on Extended Morphological Profiles and Guided Filter","volume":"2","author":"Mokhtarzade","year":"2020","journal-title":"Comput. Knowl. Eng."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"042603","DOI":"10.1117\/1.JRS.15.042603","article-title":"Study on ground object classification based on the hyperspectral fusion images of ZY-1 (02D) satellite","volume":"15","author":"Yu","year":"2021","journal-title":"J. Appl. Remote Sens."},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Lu, H., Qiao, D., Li, Y., Wu, S., and Deng, L. (2021). Fusion of China ZY-1 02D Hyperspectral Data and Multispectral Data: Which Methods Should Be Used?. Remote Sens., 13.","DOI":"10.3390\/rs13122354"},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Xu, Z., Chen, S., Zhu, B., Chen, L., Ye, Y., and Lu, P. (2022). Evaluating the Capability of Satellite Hyperspectral Imager, the ZY1\u201302D, for Topsoil Nitrogen Content Estimation and Mapping of Farmlands in Black Soil Area, China. Remote Sens., 14.","DOI":"10.3390\/rs14041008"},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Lyu, X., Li, X., Gong, J., Wang, H., Dang, D., Dou, H., Li, S., and Liu, S. (2020). Comprehensive grassland degradation monitoring by remote sensing in Xilinhot, Inner Mongolia, China. Sustainability, 12.","DOI":"10.3390\/su12093682"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"219","DOI":"10.1016\/j.chnaes.2017.02.009","article-title":"Grassland degradation and restoration monitoring and driving forces analysis based on long time-series remote sensing data in Xilin Gol League","volume":"37","author":"Sun","year":"2017","journal-title":"Acta Ecol. Sin."},{"key":"ref_47","unstructured":"Zhang, X., Sun, S., Yong, S., Zhou, Z., and Wang, R. (2007). Vegetation map of the People\u2019s Republic of China (1:1,000,000). Geol. Publ. House."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"1125","DOI":"10.1016\/j.scib.2020.04.004","article-title":"An updated vegetation map of China (1:1,000,000)","volume":"65","author":"Su","year":"2020","journal-title":"Sci. Bull."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"114593","DOI":"10.1016\/j.jenvman.2022.114593","article-title":"Effectiveness of functional zones in National Nature Reserves for the protection of forest ecosystems in China","volume":"308","author":"Liu","year":"2022","journal-title":"J. Environ. Manag."},{"key":"ref_50","first-page":"e01708","article-title":"Data-driven planning adjustments of the functional zoning of Houhe National Nature Reserve","volume":"29","author":"Tang","year":"2021","journal-title":"Glob. Ecol. Conserv."},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Kherif, F., and Latypova, A. (2020). Principal Component Analysis. Machine Learning, Elsevier.","DOI":"10.1016\/B978-0-12-815739-8.00012-2"},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"480","DOI":"10.1109\/TGRS.2004.842478","article-title":"Classification of hyperspectral data from urban areas based on extended morphological profiles","volume":"43","author":"Benediktsson","year":"2005","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"257","DOI":"10.1109\/TGRS.2012.2202912","article-title":"An SVM ensemble approach combining spectral, structural, and semantic features for the classification of high-resolution remotely sensed imagery","volume":"51","author":"Huang","year":"2012","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Lee, H., and Kwon, H. (2016, January 10\u201315). Contextual deep CNN based hyperspectral classification. Proceedings of the 2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Beijing, China.","DOI":"10.1109\/IGARSS.2016.7729859"},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"Li, Y., Zhang, H., and Shen, Q. (2017). Spectral\u2013spatial classification of hyperspectral imagery with 3D convolutional neural network. Remote Sens., 9.","DOI":"10.3390\/rs9010067"},{"key":"ref_56","doi-asserted-by":"crossref","unstructured":"Zhao, R., Li, Y., and Ma, M. (2021). Mapping paddy rice with satellite remote sensing: A review. Sustainability, 13.","DOI":"10.3390\/su13020503"},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"107822","DOI":"10.1016\/j.ecolind.2021.107822","article-title":"Satellite remote sensing to assess cyanobacterial bloom frequency across the United States at multiple spatial scales","volume":"128","author":"Coffer","year":"2021","journal-title":"Ecol. Indic."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/15\/3751\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T00:04:31Z","timestamp":1760141071000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/15\/3751"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,8,5]]},"references-count":57,"journal-issue":{"issue":"15","published-online":{"date-parts":[[2022,8]]}},"alternative-id":["rs14153751"],"URL":"https:\/\/doi.org\/10.3390\/rs14153751","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,8,5]]}}}