{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,10]],"date-time":"2026-02-10T16:57:44Z","timestamp":1770742664406,"version":"3.49.0"},"reference-count":43,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2024,1,17]],"date-time":"2024-01-17T00:00:00Z","timestamp":1705449600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Key Research and Development Program of China","award":["2019YFE0197800"],"award-info":[{"award-number":["2019YFE0197800"]}]},{"name":"National Key Research and Development Program of China","award":["2022FY100204"],"award-info":[{"award-number":["2022FY100204"]}]},{"name":"Science and Technology Fundamental Resources Investigation Program","award":["2019YFE0197800"],"award-info":[{"award-number":["2019YFE0197800"]}]},{"name":"Science and Technology Fundamental Resources Investigation Program","award":["2022FY100204"],"award-info":[{"award-number":["2022FY100204"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Shrubs are important ecological barriers in desert regions and an important component of global carbon estimation. However, the shrubland in deserts has been hardly presented, although many high-quality land cover datasets with a 10 m scale based on remote-sensing data have been publicly released products. Therefore, the underestimation of carbon storage is inevitable with the absence of desert shrublands. The existing land-cover datasets have been analyzed and compared, and it has been found that the reason for missing the shrubland in deserts is mainly indued by the absence of shrubland samples, which are easy to neglect and difficult to retrieve. In this study, we developed a semi-automatic method to extract shrubland samples in deserts as the updated input for the machine-learning method. Firstly, the initial samples of desert shrublands were identified from the very high spatial-resolution (0.3~0.5 m) imagery on GEE, and the maximum NDVI from Sentinel-2 was used for double-checking. Secondly, a feature-based method was used to learn the feature from the initial samples and a similarity-based searching method was employed to automatically expand the samples. Finally, the expanded samples and their corresponding time-series satellite images were inputted into different machine-learning methods at a large region (1.63 \u00d7 106 km2) for extracting the shrubland in the desert. It was found that different combinations of feature variables and time-series combinations have different impacts on the overall accuracy (OA) of the classification results, as well as the performance of identifying and classifying the different land-cover types. Compared to the existing global-scale land-cover products, the proposed method can better identify the shrubland in deserts and show better overall accuracy.<\/jats:p>","DOI":"10.3390\/rs16020374","type":"journal-article","created":{"date-parts":[[2024,1,17]],"date-time":"2024-01-17T07:41:28Z","timestamp":1705477288000},"page":"374","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Extracting Shrubland in Deserts from Medium-Resolution Remote-Sensing Data at Large Scale"],"prefix":"10.3390","volume":"16","author":[{"given":"Bo","family":"Zhong","sequence":"first","affiliation":[{"name":"College of Computer Science and Technology, Chongqing University of Posts and Telecommunications, Chongqing 400065, China"},{"name":"State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Li","family":"Yang","sequence":"additional","affiliation":[{"name":"College of Computer Science and Technology, Chongqing University of Posts and Telecommunications, Chongqing 400065, China"},{"name":"State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5688-0324","authenticated-orcid":false,"given":"Xiaobo","family":"Luo","sequence":"additional","affiliation":[{"name":"College of Computer Science and Technology, Chongqing University of Posts and Telecommunications, Chongqing 400065, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Junjun","family":"Wu","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Longfei","family":"Hu","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2024,1,17]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"111516","DOI":"10.1016\/j.rse.2019.111516","article-title":"Improved Mapping and Understanding of Desert Vegetation-Habitat Complexes from Intraannual Series of Spectral Endmember Space Using Cross-Wavelet Transform and Logistic Regression","volume":"236","author":"Sun","year":"2020","journal-title":"Remote Sens. Environ."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"330","DOI":"10.1016\/j.catena.2018.10.016","article-title":"Effects of Shrub Species on Soil Nitrogen Mineralization in the Desert-Loess Transition Zone","volume":"173","author":"Yao","year":"2019","journal-title":"Catena"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"301","DOI":"10.1016\/S0305-9006(03)00066-7","article-title":"Remote Sensing Technology for Mapping and Monitoring Land-Cover and Land-Use Change","volume":"61","author":"Rogan","year":"2004","journal-title":"Prog. Plan."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"28","DOI":"10.1016\/j.rse.2015.10.036","article-title":"Woody Plant Cover Estimation in Drylands from Earth Observation Based Seasonal Metrics","volume":"172","author":"Brandt","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"572","DOI":"10.1016\/j.rse.2018.07.025","article-title":"Estimating the Age and Population Structure of Encroaching Shrubs in Arid\/Semiarid Grasslands Using High Spatial Resolution Remote Sensing Imagery","volume":"216","author":"Cao","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Zhou, H., Fu, L., Sharma, R.P., Lei, Y., and Guo, J. (2021). A Hybrid Approach of Combining Random Forest with Texture Analysis and VDVI for Desert Vegetation Mapping Based on UAV RGB Data. Remote Sens., 13.","DOI":"10.3390\/rs13101891"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"101852","DOI":"10.1016\/j.ecoinf.2022.101852","article-title":"Classification of Desert Grassland Species Based on a Local-Global Feature Enhancement Network and UAV Hyperspectral Remote Sensing","volume":"72","author":"Zhang","year":"2022","journal-title":"Ecol. Inform."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"107494","DOI":"10.1016\/j.ecolind.2021.107494","article-title":"An Improved Approach to Estimate Above-Ground Volume and Biomass of Desert Shrub Communities Based on UAV RGB Images","volume":"125","author":"Mao","year":"2021","journal-title":"Ecol. Indic."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"389","DOI":"10.1007\/s10661-020-08330-1","article-title":"A Comparative Study of Remote Sensing Classification Methods for Monitoring and Assessing Desert Vegetation Using a UAV-Based Multispectral Sensor","volume":"192","author":"Abdullah","year":"2020","journal-title":"Environ. Monit. Assess."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"105614","DOI":"10.1016\/j.ecolind.2019.105614","article-title":"Identification and Assessment of the Factors Driving Vegetation Degradation\/Regeneration in Drylands Using Synthetic High Spatiotemporal Remote Sensing Data\u2014A Case Study in Zhenglanqi, Inner Mongolia, China","volume":"107","author":"Sun","year":"2019","journal-title":"Ecol. Indic."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"7221","DOI":"10.5846\/stxb201208101131","article-title":"Effects of Shrub Encroachment on Biomass and Biodiversity in the Typical Steppe of Inner Mongolia","volume":"33","author":"Peng","year":"2013","journal-title":"Acta Ecol. Sin."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"198","DOI":"10.1016\/j.rse.2004.07.011","article-title":"Object-Oriented Image Analysis for Mapping Shrub Encroachment from 1937 to 2003 in Southern New Mexico","volume":"93","author":"Laliberte","year":"2004","journal-title":"Remote Sens. Environ."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"355","DOI":"10.1657\/1938-4246-43.3.355","article-title":"Shrub Cover on the North Slope of Alaska: A circa 2000 Baseline Map","volume":"43","author":"Beck","year":"2011","journal-title":"Arct. Antarct. Alp. Res."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"201","DOI":"10.1016\/j.rse.2018.06.044","article-title":"Mapping Continuous Fields of Tree and Shrub Cover across the Gran Chaco Using Landsat 8 and Sentinel-1 Data","volume":"216","author":"Baumann","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Bayle, A., Carlson, B.Z., Thierion, V., Isenmann, M., and Choler, P. (2019). Improved Mapping of Mountain Shrublands Using the Sentinel-2 Red-Edge Band. Remote Sens., 11.","DOI":"10.3390\/rs11232807"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"6709","DOI":"10.3390\/rs6076709","article-title":"Predictive Mapping of Dwarf Shrub Vegetation in an Arid High Mountain Ecosystem Using Remote Sensing and Random Forests","volume":"6","author":"Vanselow","year":"2014","journal-title":"Remote Sens."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1016\/j.scib.2019.03.002","article-title":"Stable Classification with Limited Sample: Transferring a 30-m Resolution Sample Set Collected in 2015 to Mapping 10-m Resolution Global Land Cover in 2017","volume":"64","author":"Gong","year":"2019","journal-title":"Sci. Bull."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"2753","DOI":"10.5194\/essd-13-2753-2021","article-title":"GLC_FCS30: Global Land-Cover Product with Fine Classification System at 30 m Using Time-Series Landsat Imagery","volume":"13","author":"Zhang","year":"2021","journal-title":"Earth Syst. Sci. Data"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"7","DOI":"10.1016\/j.isprsjprs.2014.09.002","article-title":"Global Land Cover Mapping at 30m Resolution: A POK-Based Operational Approach","volume":"103","author":"Chen","year":"2015","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_20","unstructured":"Zanaga, D., Van De Kerchove, R., De Keersmaecker, W., Souverijns, N., Brockmann, C., Quast, R., Wevers, J., Grosu, A., Paccini, A., and Vergnaud, S. (2023, June 01). ESA WorldCover 10 m 2020 V100 2021. Available online: https:\/\/zenodo.org\/records\/5571936."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"25","DOI":"10.1016\/j.rse.2011.11.026","article-title":"Sentinel-2: ESA\u2019s Optical High-Resolution Mission for GMES Operational Services","volume":"120","author":"Drusch","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s11676-020-01155-1","article-title":"A Commentary Review on the Use of Normalized Difference Vegetation Index (NDVI) in the Era of Popular Remote Sensing","volume":"32","author":"Huang","year":"2021","journal-title":"J. For. Res."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"52","DOI":"10.1016\/j.agrformet.2014.06.007","article-title":"A Comparative Analysis of Multitemporal MODIS EVI and NDVI Data for Large-Scale Rice Yield Estimation","volume":"197","author":"Son","year":"2014","journal-title":"Agric. For. Meteorol."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"257","DOI":"10.1016\/S0034-4257(96)00067-3","article-title":"NDWI\u2014A Normalized Difference Water Index for Remote Sensing of Vegetation Liquid Water from Space","volume":"58","author":"Gao","year":"1996","journal-title":"Remote Sens. Environ."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Rasul, A., Balzter, H., Ibrahim, G.R.F., Hameed, H.M., Wheeler, J., Adamu, B., Ibrahim, S., and Najmaddin, P.M. (2018). Applying Built-Up and Bare-Soil Indices from Landsat 8 to Cities in Dry Climates. Land, 7.","DOI":"10.3390\/land7030081"},{"key":"ref_26","first-page":"256","article-title":"Bi-Temporal Characterization of Land Surface Temperature in Relation to Impervious Surface Area, NDVI and NDBI, Using a Sub-Pixel Image Analysis","volume":"11","author":"Zhang","year":"2009","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Gonenc, A., Ozerdem, M.S., and Acar, E. (2019, January 16\u201319). Comparison of NDVI and RVI Vegetation Indices Using Satellite Images. Proceedings of the 2019 8th International Conference on Agro-Geoinformatics (Agro-Geoinformatics), Istanbul, Turkey.","DOI":"10.1109\/Agro-Geoinformatics.2019.8820225"},{"key":"ref_28","first-page":"758","article-title":"A Review on Random Forest: An Ensemble Classifier","volume":"Volume 26","author":"Hemanth","year":"2019","journal-title":"International Conference on Intelligent Data Communication Technologies and Internet of Things (ICICI) 2018"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"24","DOI":"10.1016\/j.isprsjprs.2016.01.011","article-title":"Random Forest in Remote Sensing: A Review of Applications and Future Directions","volume":"114","author":"Belgiu","year":"2016","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"78","DOI":"10.1016\/j.isprsjprs.2012.04.001","article-title":"Comparison of Support Vector Machine, Neural Network, and CART Algorithms for the Land-Cover Classification Using Limited Training Data Points","volume":"70","author":"Shao","year":"2012","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_31","unstructured":"Bittencourt, H.R., and Clarke, R.T. (2003, January 21\u201325). Use of Classification and Regression Trees (CART) to Classify Remotely-Sensed Digital Images. Proceedings of the IGARSS 2003. 2003 IEEE International Geoscience and Remote Sensing Symposium. Proceedings (IEEE Cat. No.03CH37477), Toulouse, France."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"35","DOI":"10.1016\/0034-4257(91)90048-B","article-title":"A Review of Assessing the Accuracy of Classifications of Remotely Sensed Data","volume":"37","author":"Congalton","year":"1991","journal-title":"Remote Sens. Environ."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"18","DOI":"10.1016\/j.rse.2017.06.031","article-title":"Google Earth Engine: Planetary-Scale Geospatial Analysis for Everyone","volume":"202","author":"Gorelick","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"2895","DOI":"10.1080\/01431160500185227","article-title":"Some Issues in the Classification of DAIS Hyperspectral Data","volume":"27","author":"Pal","year":"2006","journal-title":"Int. J. Remote Sens."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"89","DOI":"10.1016\/j.rse.2017.04.003","article-title":"Obtaining Rubber Plantation Age Information from Very Dense Landsat TM & ETM+ Time Series Data and Pixel-Based Image Compositing","volume":"196","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Xie, S., Liu, L., Zhang, X., Yang, J., Chen, X., and Gao, Y. (2019). Automatic Land-Cover Mapping Using Landsat Time-Series Data Based on Google Earth Engine. Remote Sens., 11.","DOI":"10.3390\/rs11243023"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"111511","DOI":"10.1016\/j.rse.2019.111511","article-title":"A Review of Vegetation Phenological Metrics Extraction Using Time-Series, Multispectral Satellite Data","volume":"237","author":"Zeng","year":"2020","journal-title":"Remote Sens. Environ."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"2088","DOI":"10.1109\/JSTARS.2012.2228167","article-title":"A Pixel-Based Landsat Compositing Algorithm for Large Area Land Cover Mapping","volume":"6","author":"Griffiths","year":"2013","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"67","DOI":"10.1080\/07038992.2018.1437719","article-title":"Disturbance-Informed Annual Land cover Classification Maps of Canada\u2019s Forested Ecosystems for a 29-Year Landsat Time Series","volume":"44","author":"Hermosilla","year":"2018","journal-title":"Can. J. Remote Sens."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"2249","DOI":"10.1016\/j.csda.2007.08.015","article-title":"Empirical Characterization of Random Forest Variable Importance Measures","volume":"52","author":"Archer","year":"2008","journal-title":"Comput. Stat. Data Anal."},{"key":"ref_41","first-page":"100351","article-title":"Performance Evaluation of MLE, RF and SVM Classification Algorithms for Watershed Scale Land Use\/Land Cover Mapping Using Sentinel 2 Bands","volume":"19","author":"Rana","year":"2020","journal-title":"Remote Sens. Appl. Soc. Environ."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"247","DOI":"10.1016\/j.isprsjprs.2010.11.001","article-title":"Support Vector Machines in Remote Sensing: A Review","volume":"66","author":"Mountrakis","year":"2011","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Zhang, Z., Wei, M., Pu, D., He, G., Wang, G., and Long, T. (2021). Assessment of Annual Composite Images Obtained by Google Earth Engine for Urban Areas Mapping Using Random Forest. Remote Sens., 13.","DOI":"10.3390\/rs13040748"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/2\/374\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T13:48:44Z","timestamp":1760104124000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/2\/374"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,1,17]]},"references-count":43,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2024,1]]}},"alternative-id":["rs16020374"],"URL":"https:\/\/doi.org\/10.3390\/rs16020374","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,1,17]]}}}