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Effective LULC products that enable the monitoring of changes in regional ecosystem types are of great importance for their environmental protection and macro-control. Here, we combined an 18-class LULC classification scheme based on ecosystem types with Sentinel-2 imagery, the Google Earth Engine (GEE) platform, and the random forest method to present new LULC products with a spatial resolution of 10 m in 2018 and 2020 for the upper Yellow River Basin over the TP and conducted monitoring of changes in ecosystem types. The results indicated that: (1) In 2018 and 2020, the overall accuracy (OA) of LULC maps ranged between 87.45% and 93.02%. (2) Grassland was the main LULC first-degree class in the research area, followed by wetland and water bodies and barren land. For the LULC second-degree class, the main LULC was grassland, followed by broadleaf shrub and marsh. (3) In the first-degree class of changes in ecosystem types, the largest area of progressive succession (positive) was grassland\u2013shrubland (451.13 km2), whereas the largest area of retrogressive succession (negative) was grassland\u2013barren (395.91 km2). In the second-degree class, the largest areas of progressive succession (positive) were grassland\u2013broadleaf shrub (344.68 km2) and desert land\u2013grassland (302.02 km2), whereas the largest areas of retrogressive succession (negative) were broadleaf shrubland\u2013grassland (309.08 km2) and grassland\u2013bare rock (193.89 km2). The northern and southwestern parts of the study area showed a trend towards positive succession, whereas the south-central Huangnan, northeastern Gannan, and central Aba Prefectures showed signs of retrogressive succession in their changes in ecosystem types. The purpose of this study was to provide basis data for basin-scale ecosystem monitoring and analysis with more detailed categories and reliable accuracy.<\/jats:p>","DOI":"10.3390\/rs14215361","type":"journal-article","created":{"date-parts":[[2022,10,26]],"date-time":"2022-10-26T07:17:48Z","timestamp":1666768668000},"page":"5361","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":52,"title":["Land Use\/Land Cover Mapping Based on GEE for the Monitoring of Changes in Ecosystem Types in the Upper Yellow River Basin over the Tibetan Plateau"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0621-9498","authenticated-orcid":false,"given":"Senyao","family":"Feng","sequence":"first","affiliation":[{"name":"State Key Laboratory of Herbage Improvement and Grassland Agro-Ecosystems, College of Pastoral Agriculture Science and Technology, Lanzhou University, Lanzhou 730000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wenlong","family":"Li","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Herbage Improvement and Grassland Agro-Ecosystems, College of Pastoral Agriculture Science and Technology, Lanzhou University, Lanzhou 730000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jing","family":"Xu","sequence":"additional","affiliation":[{"name":"School of Agriculture and Forestry Economic and Management, Lanzhou University of Finance and Economics, Lanzhou 730020, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tiangang","family":"Liang","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Herbage Improvement and Grassland Agro-Ecosystems, College of Pastoral Agriculture Science and Technology, Lanzhou University, Lanzhou 730000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1499-8476","authenticated-orcid":false,"given":"Xuanlong","family":"Ma","sequence":"additional","affiliation":[{"name":"College of Earth and Environmental Sciences, Lanzhou University, Lanzhou 730020, China"},{"name":"Institute of Yellow River Basin Green Development, Lanzhou University, Lanzhou 730020, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wenying","family":"Wang","sequence":"additional","affiliation":[{"name":"Department of Life Sciences, Qinghai Normal University, Xining 810008, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hongyan","family":"Yu","sequence":"additional","affiliation":[{"name":"Service Guarantee Center of Qilian Mountain National Park in Qinghai, Xining 810008, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,10,26]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"2607","DOI":"10.1080\/01431161.2012.748992","article-title":"Finer resolution observation and monitoring of global land cover: First mapping results with Landsat TM and ETM+ data","volume":"34","author":"Gong","year":"2013","journal-title":"Int. J. Remote Sens."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"3465","DOI":"10.1073\/pnas.1100480108","article-title":"Global land use change, economic globalization, and the looming land scarcity","volume":"108","author":"Lambin","year":"2011","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"20666","DOI":"10.1073\/pnas.0704119104","article-title":"The emergence of land change science for global environmental change and sustainability","volume":"104","author":"Turner","year":"2007","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"639","DOI":"10.1038\/s41586-018-0411-9","article-title":"Global land change from 1982 to 2016","volume":"560","author":"Song","year":"2018","journal-title":"Nature"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"570","DOI":"10.1126\/science.1111772","article-title":"Global consequences of land use","volume":"309","author":"Foley","year":"2005","journal-title":"Science"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"605","DOI":"10.5194\/essd-8-605-2016","article-title":"Global carbon budget 2016","volume":"8","author":"Andrew","year":"2016","journal-title":"Earth Syst. Sci. Data"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"293","DOI":"10.1038\/nature06592","article-title":"An Earth-system perspective of the global nitrogen cycle","volume":"451","author":"Gruber","year":"2008","journal-title":"Nature"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"756","DOI":"10.1126\/science.1150195","article-title":"Global change and the ecology of cities","volume":"319","author":"Grimm","year":"2008","journal-title":"Science"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"385","DOI":"10.1038\/nclimate1690","article-title":"The impact of global land-cover change on the terrestrial water cycle","volume":"3","author":"Sterling","year":"2013","journal-title":"Nat. Clim. Change"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"127811","DOI":"10.1016\/j.jhydrol.2022.127811","article-title":"Evaluating potential impacts of land use changes on water supply-demand under multiple development scenarios in dryland region","volume":"610","author":"Liu","year":"2022","journal-title":"J. Hydrol."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"1341","DOI":"10.1073\/pnas.1111374109","article-title":"Decoupling of deforestation and soy production in the southern Amazon during the late 2000s","volume":"109","author":"Macedo","year":"2012","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"14637","DOI":"10.1073\/pnas.0606377103","article-title":"Cropland expansion changes deforestation dynamics in the southern Brazilian Amazon","volume":"103","author":"Morton","year":"2006","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"1561","DOI":"10.1016\/j.scitotenv.2017.12.143","article-title":"Detecting the response of bird communities and biodiversity to habitat loss and fragmentation due to urbanization","volume":"624","author":"Xu","year":"2018","journal-title":"Sci. Total Environ."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Llerena-Montoya, S., Velastegui-Montoya, A., Zhirzhan-Azanza, B., Herrera-Matamoros, V., Adami, M., de Lima, A., Moscoso-Silva, F., and Encalada, L. (2021). Multitemporal analysis of land use and land cover within an oil block in the Ecuadorian Amazon. ISPRS Int. J. Geo-Inf., 10.","DOI":"10.3390\/ijgi10030191"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"104","DOI":"10.1890\/07-1117.1","article-title":"Forest to reclaimed mine land use change leads to altered ecosystem structure and function","volume":"18","author":"Simmons","year":"2008","journal-title":"Ecol. Appl."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"45","DOI":"10.1038\/nature14324","article-title":"Global effects of land use on local terrestrial biodiversity","volume":"520","author":"Newbold","year":"2015","journal-title":"Nature"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"441","DOI":"10.1038\/nature23285","article-title":"Global forest loss disproportionately erodes biodiversity in intact landscapes","volume":"547","author":"Betts","year":"2017","journal-title":"Nature"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"128968","DOI":"10.1016\/j.jclepro.2021.128968","article-title":"Land cover pattern and habitat suitability on the global largest breeding sites for Black-necked Cranes","volume":"322","author":"Hou","year":"2021","journal-title":"J. Clean. Prod."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Nelson, E., Sander, H., Hawthorne, P., Conte, M., Ennaanay, D., Wolny, S., Manson, S., and Polasky, S. (2010). Projecting global land-use change and its effect on ecosystem service provision and biodiversity with simple models. PLoS ONE, 5.","DOI":"10.1371\/journal.pone.0014327"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"788","DOI":"10.1126\/science.aap9565","article-title":"One-third of global protected land is under intense human pressure","volume":"360","author":"Jones","year":"2018","journal-title":"Science"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Tassi, A., Gigante, D., Modica, G., Di Martino, L., and Vizzari, M. (2021). Pixel-vs. Object-based landsat 8 data classification in google earth engine using random forest: The case study of maiella national park. Remote Sens., 13.","DOI":"10.3390\/rs13122299"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Liu, C., Li, W., Zhu, G., Zhou, H., Yan, H., and Xue, P. (2020). Land use\/land cover changes and their driving factors in the Northeastern Tibetan Plateau based on Geographical Detectors and Google Earth Engine: A case study in Gannan Prefecture. Remote Sens., 12.","DOI":"10.3390\/rs12193139"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"112034","DOI":"10.1016\/j.rse.2020.112034","article-title":"Towards a comprehensive and consistent global aquatic land cover characterization framework addressing multiple user needs","volume":"250","author":"Xu","year":"2020","journal-title":"Remote Sens. Environ."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"7","DOI":"10.1016\/j.isprsjprs.2014.09.002","article-title":"Global land cover mapping at 30 m resolution: A POK-based operational approach","volume":"103","author":"Chen","year":"2015","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_25","first-page":"102688","article-title":"A 30 m-resolution land use-land cover product for the Colombian Andes and Amazon using cloud-computing","volume":"107","author":"Clerici","year":"2022","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Meng, B., Zhang, Y., Yang, Z., Lv, Y., Chen, J., Li, M., Sun, Y., Zhang, H., Yu, H., and Zhang, J. (2022). Mapping Grassland Classes Using Unmanned Aerial Vehicle and MODIS NDVI Data for Temperate Grassland in Inner Mongolia, China. Remote Sens., 14.","DOI":"10.3390\/rs14092094"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Yao, J., Wu, J., Xiao, C., Zhang, Z., and Li, J. (2022). The Classification Method Study of Crops Remote Sensing with Deep Learning, Machine Learning, and Google Earth Engine. Remote Sens., 14.","DOI":"10.3390\/rs14122758"},{"key":"ref_28","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_29","doi-asserted-by":"crossref","first-page":"73","DOI":"10.1016\/j.rse.2018.04.050","article-title":"Urban land-use mapping using a deep convolutional neural network with high spatial resolution multispectral remote sensing imagery","volume":"214","author":"Huang","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"154","DOI":"10.1016\/j.rse.2014.02.001","article-title":"Landsat-8: Science and product vision for terrestrial global change research","volume":"145","author":"Roy","year":"2014","journal-title":"Remote Sens. Environ."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"2094","DOI":"10.1109\/JSTARS.2014.2329330","article-title":"Deep learning-based classification of hyperspectral data","volume":"7","author":"Chen","year":"2014","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"044056","DOI":"10.1088\/1748-9326\/ac6006","article-title":"Patterns and causes of winter wheat and summer maize rotation area change over the North China Plain","volume":"17","author":"Liu","year":"2022","journal-title":"Environ. Res. Lett."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/s41597-021-00827-9","article-title":"The 10-m crop type maps in Northeast China during 2017\u20132019","volume":"8","author":"You","year":"2021","journal-title":"Sci. Data"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"104190","DOI":"10.1016\/j.landusepol.2019.104190","article-title":"Mapping sugarcane in complex landscapes by integrating multi-temporal Sentinel-2 images and machine learning algorithms","volume":"88","author":"Wang","year":"2019","journal-title":"Land Use Policy"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"331","DOI":"10.1080\/15481603.2017.1370169","article-title":"Landsat-8 vs. Sentinel-2: Examining the added value of sentinel-2\u2032s red-edge bands to land-use and land-cover mapping in Burkina Faso","volume":"55","author":"Forkuor","year":"2018","journal-title":"Giscience Remote Sens."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"115","DOI":"10.1016\/j.rse.2019.04.016","article-title":"Smallholder maize area and yield mapping at national scales with Google Earth Engine","volume":"228","author":"Jin","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"47","DOI":"10.1016\/j.future.2014.10.029","article-title":"Remote sensing big data computing: Challenges and opportunities","volume":"51","author":"Ma","year":"2015","journal-title":"Future Gener. Comput. Syst."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"152","DOI":"10.1016\/j.isprsjprs.2020.04.001","article-title":"Google Earth Engine for geo-big data applications: A meta-analysis and systematic review","volume":"164","author":"Tamiminia","year":"2020","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_39","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_40","first-page":"102518","article-title":"Monitoring three-decade dynamics of citrus planting in Southeastern China using dense Landsat records","volume":"103","author":"Xu","year":"2021","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"162","DOI":"10.1016\/j.rse.2018.09.019","article-title":"Modeling alpine grassland cover based on MODIS data and support vector machine regression in the headwater region of the Huanghe River, China","volume":"218","author":"Ge","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Ji, Q., Liang, W., Fu, B., Zhang, W., Yan, J., L\u00fc, Y., Yue, C., Jin, Z., Lan, Z., and Li, S. (2021). Mapping land use\/cover dynamics of the Yellow River Basin from 1986 to 2018 supported by Google Earth Engine. Remote Sens., 13.","DOI":"10.3390\/rs13071299"},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Hou, M., Ge, J., Gao, J., Meng, B., Li, Y., Yin, J., Liu, J., Feng, Q., and Liang, T. (2020). Ecological risk assessment and impact factor analysis of alpine wetland ecosystem based on LUCC and boosted regression tree on the Zoige Plateau, China. Remote Sens., 12.","DOI":"10.3390\/rs12030368"},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Wohlfart, C., Liu, G., Huang, C., and Kuenzer, C. (2016). A River Basin over the course of time: Multi-temporal analyses of land surface dynamics in the Yellow River Basin (China) based on medium resolution remote sensing data. Remote Sens., 8.","DOI":"10.3390\/rs8030186"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"111268","DOI":"10.1016\/j.rse.2019.111268","article-title":"AMSR2 snow depth downscaling algorithm based on a multifactor approach over the Tibetan Plateau, China","volume":"231","author":"Wang","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Ye, C., Sun, J., Liu, M., Xiong, J., Zong, N., Hu, J., Huang, Y., Duan, X., and Tsunekawa, A. (2020). Concurrent and lagged effects of extreme drought induce net reduction in vegetation carbon uptake on Tibetan Plateau. Remote Sens., 12.","DOI":"10.3390\/rs12152347"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"193","DOI":"10.1038\/s41586-018-0280-2","article-title":"China\u2019s response to a national land-system sustainability emergency","volume":"559","author":"Bryan","year":"2018","journal-title":"Nature"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"370","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_49","doi-asserted-by":"crossref","first-page":"682","DOI":"10.1007\/s13280-012-0290-5","article-title":"Socio-economic impacts on flooding: A 4000-year history of the Yellow River, China","volume":"41","author":"Chen","year":"2012","journal-title":"Ambio"},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Yang, Y., Yang, D., Wang, X., Zhang, Z., and Nawaz, Z. (2021). Testing accuracy of land cover classification algorithms in the qilian mountains based on gee cloud platform. Remote Sens., 13.","DOI":"10.3390\/rs13245064"},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"362","DOI":"10.1016\/j.isprsjprs.2020.03.017","article-title":"Potential of hyperspectral data and machine learning algorithms to estimate the forage carbon-nitrogen ratio in an alpine grassland ecosystem of the Tibetan Plateau","volume":"163","author":"Gao","year":"2020","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Ma, Y., Huang, X., Feng, Q., and Liang, T. (2022). Alpine Grassland Reviving Response to Seasonal Snow Cover on the Tibetan Plateau. Remote Sens., 14.","DOI":"10.3390\/rs14102499"},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"112885","DOI":"10.1016\/j.rse.2021.112885","article-title":"Aboveground biomass of salt-marsh vegetation in coastal wetlands: Sample expansion of in situ hyperspectral and Sentinel-2 data using a generative adversarial network","volume":"270","author":"Chen","year":"2022","journal-title":"Remote Sens. Environ."},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Zekoll, V., Main-Knorn, M., Alonso, K., Louis, J., Frantz, D., Richter, R., and Pflug, B. (2021). Comparison of masking algorithms for sentinel-2 imagery. Remote Sens., 13.","DOI":"10.3390\/rs13010137"},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"113078","DOI":"10.1016\/j.rse.2022.113078","article-title":"Indices enhance biological soil crust mapping in sandy and desert lands","volume":"278","author":"Wang","year":"2022","journal-title":"Remote Sens. Environ."},{"key":"ref_56","doi-asserted-by":"crossref","unstructured":"Xi, Z., Xu, H., Xing, Y., Gong, W., Chen, G., and Yang, S. (2022). Forest Canopy Height Mapping by Synergizing ICESat-2, Sentinel-1, Sentinel-2 and Topographic Information Based on Machine Learning Methods. Remote Sens., 14.","DOI":"10.3390\/rs14020364"},{"key":"ref_57","doi-asserted-by":"crossref","unstructured":"Farr, T.G., Rosen, P.A., Caro, E., Crippen, R., Duren, R., Hensley, S., Kobrick, M., Paller, M., Rodriguez, E., and Roth, L. (2007). The shuttle radar topography mission. Rev. Geophys., 45.","DOI":"10.1029\/2005RG000183"},{"key":"ref_58","doi-asserted-by":"crossref","unstructured":"Meng, B., Yang, Z., Yu, H., Qin, Y., Sun, Y., Zhang, J., Chen, J., Wang, Z., Zhang, W., and Li, M. (2021). Mapping of Kobresia pygmaea Community Based on Umanned Aerial Vehicle Technology and Gaofen Remote Sensing Data in Alpine Meadow Grassland: A Case Study in Eastern of Qinghai\u2013Tibetan Plateau. Remote Sens., 13.","DOI":"10.3390\/rs13132483"},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"166","DOI":"10.1016\/j.rse.2017.02.021","article-title":"Mapping major land cover dynamics in Beijing using all Landsat images in Google Earth Engine","volume":"202","author":"Huang","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_60","doi-asserted-by":"crossref","unstructured":"Dubertret, F., Le Tourneau, F.-M., Villarreal, M.L., and Norman, L.M. (2022). Monitoring Annual Land Use\/Land Cover Change in the Tucson Metropolitan Area with Google Earth Engine (1986\u20132020). Remote Sens., 14.","DOI":"10.3390\/rs14092127"},{"key":"ref_61","doi-asserted-by":"crossref","unstructured":"Wa\u015bniewski, A., Ho\u015bci\u0142o, A., and Chmielewska, M. (2022). Can a Hierarchical Classification of Sentinel-2 Data Improve Land Cover Mapping?. Remote Sens., 14.","DOI":"10.3390\/rs14040989"},{"key":"ref_62","first-page":"219","article-title":"An ecosystem classification system based on remote sensor information in China","volume":"35","author":"Ouyang","year":"2015","journal-title":"Acta Ecol. Sin."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"1303","DOI":"10.1080\/014311600210191","article-title":"Development of a global land cover characteristics database and IGBP DISCover from 1 km AVHRR data","volume":"21","author":"Loveland","year":"2000","journal-title":"Int. J. Remote Sens."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"12070","DOI":"10.3390\/rs61212070","article-title":"Global land cover mapping: A review and uncertainty analysis","volume":"6","author":"Congalton","year":"2014","journal-title":"Remote Sens."},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"2401","DOI":"10.3390\/rs70302401","article-title":"Development of Decadal (1985\u20131995\u20132005) Land Use and Land Cover Database for India","volume":"7","author":"Roy","year":"2015","journal-title":"Remote Sens."},{"key":"ref_66","doi-asserted-by":"crossref","unstructured":"Vizzari, M. (2022). PlanetScope, Sentinel-2, and Sentinel-1 Data Integration for Object-Based Land Cover Classification in Google Earth Engine. Remote Sens., 14.","DOI":"10.3390\/rs14112628"},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"196","DOI":"10.1016\/j.isprsjprs.2021.04.015","article-title":"Satellite-based data fusion crop type classification and mapping in Rio Grande do Sul, Brazil","volume":"176","author":"Pott","year":"2021","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_68","doi-asserted-by":"crossref","unstructured":"Verde, N., Kokkoris, I.P., Georgiadis, C., Kaimaris, D., Dimopoulos, P., Mitsopoulos, I., and Mallinis, G. (2020). National scale land cover classification for ecosystem services mapping and assessment, using multitemporal copernicus EO data and google earth engine. Remote Sens., 12.","DOI":"10.3390\/rs12203303"},{"key":"ref_69","doi-asserted-by":"crossref","unstructured":"Tuvdendorj, B., Zeng, H., Wu, B., Elnashar, A., Zhang, M., Tian, F., Nabil, M., Nanzad, L., Bulkhbai, A., and Natsagdorj, N. (2022). Performance and the Optimal Integration of Sentinel-1\/2 Time-Series Features for Crop Classification in Northern Mongolia. Remote Sens., 14.","DOI":"10.3390\/rs14081830"},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"113040","DOI":"10.1016\/j.rse.2022.113040","article-title":"Site-specific scaling of remote sensing-based estimates of woody cover and aboveground biomass for mapping long-term tropical dry forest degradation status","volume":"276","author":"Fremout","year":"2022","journal-title":"Remote Sens. Environ."},{"key":"ref_71","doi-asserted-by":"crossref","unstructured":"Amini, S., Saber, M., Rabiei-Dastjerdi, H., and Homayouni, S. (2022). Urban Land Use and Land Cover Change Analysis Using Random Forest Classification of Landsat Time Series. Remote Sens., 14.","DOI":"10.3390\/rs14112654"},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"127","DOI":"10.1016\/0034-4257(79)90013-0","article-title":"Red and photographic infrared linear combinations for monitoring vegetation","volume":"8","author":"Tucker","year":"1979","journal-title":"Remote Sens. Environ."},{"key":"ref_73","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_74","doi-asserted-by":"crossref","first-page":"583","DOI":"10.1080\/01431160304987","article-title":"Use of normalized difference built-up index in automatically mapping urban areas from TM imagery","volume":"24","author":"Zha","year":"2003","journal-title":"Int. J. Remote Sens."},{"key":"ref_75","doi-asserted-by":"crossref","first-page":"295","DOI":"10.1016\/0034-4257(88)90106-X","article-title":"A soil-adjusted vegetation index (SAVI)","volume":"25","author":"Huete","year":"1988","journal-title":"Remote Sens. Environ."},{"key":"ref_76","doi-asserted-by":"crossref","first-page":"195","DOI":"10.1016\/S0034-4257(02)00096-2","article-title":"Overview of the radiometric and biophysical performance of the MODIS vegetation indices","volume":"83","author":"Huete","year":"2002","journal-title":"Remote Sens. Environ."},{"key":"ref_77","doi-asserted-by":"crossref","first-page":"71","DOI":"10.14358\/PERS.72.1.71","article-title":"Mapping structural parameters and species composition of riparian vegetation using IKONOS and Landsat ETM+ data in Australian tropical savannahs","volume":"72","author":"Johansen","year":"2006","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_78","doi-asserted-by":"crossref","first-page":"64","DOI":"10.1016\/j.rse.2016.08.013","article-title":"Review of studies on tree species classification from remotely sensed data","volume":"186","author":"Fassnacht","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_79","doi-asserted-by":"crossref","unstructured":"Mohammadpour, P., Viegas, D.X., and Viegas, C. (2022). Vegetation Mapping with Random Forest Using Sentinel 2 and GLCM Texture Feature\u2014A Case Study for Lous\u00e3 Region, Portugal. Remote Sens., 14.","DOI":"10.3390\/rs14184585"},{"key":"ref_80","doi-asserted-by":"crossref","first-page":"610","DOI":"10.1109\/TSMC.1973.4309314","article-title":"Textural features for image classification","volume":"SMC-3","author":"Haralick","year":"1973","journal-title":"IEEE Trans. Syst. Man Cybern."},{"key":"ref_81","doi-asserted-by":"crossref","unstructured":"Kupidura, P. (2019). The comparison of different methods of texture analysis for their efficacy for land use classification in satellite imagery. Remote Sens., 11.","DOI":"10.3390\/rs11101233"},{"key":"ref_82","doi-asserted-by":"crossref","unstructured":"Tassi, A., and Vizzari, M. (2020). Object-oriented lulc classification in google earth engine combining snic, glcm, and machine learning algorithms. Remote Sens., 12.","DOI":"10.3390\/rs12223776"},{"key":"ref_83","doi-asserted-by":"crossref","unstructured":"Zhang, X., Zeraatpisheh, M., Rahman, M.M., Wang, S., and Xu, M. (2021). Texture is important in improving the accuracy of mapping photovoltaic power plants: A case study of Ningxia Autonomous Region, China. Remote Sens., 13.","DOI":"10.3390\/rs13193909"},{"key":"ref_84","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1023\/A:1010933404324","article-title":"Random forests","volume":"45","author":"Breiman","year":"2001","journal-title":"Mach. Learn."},{"key":"ref_85","doi-asserted-by":"crossref","first-page":"156","DOI":"10.1016\/j.rse.2016.10.010","article-title":"Assessing the robustness of Random Forests to map land cover with high resolution satellite image time series over large areas","volume":"187","author":"Pelletier","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_86","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_87","doi-asserted-by":"crossref","first-page":"112743","DOI":"10.1016\/j.rse.2021.112743","article-title":"Mapping temperate forest tree species using dense Sentinel-2 time series","volume":"267","author":"Hemmerling","year":"2021","journal-title":"Remote Sens. Environ."},{"key":"ref_88","doi-asserted-by":"crossref","unstructured":"Wei, P., Zhu, W., Zhao, Y., Fang, P., Zhang, X., Yan, N., and Zhao, H. (2021). Extraction of Kenyan Grassland Information Using PROBA-V Based on RFE-RF Algorithm. Remote Sens., 13.","DOI":"10.3390\/rs13234762"},{"key":"ref_89","doi-asserted-by":"crossref","first-page":"93","DOI":"10.1016\/j.isprsjprs.2011.11.002","article-title":"An assessment of the effectiveness of a random forest classifier for land-cover classification","volume":"67","author":"Ghimire","year":"2012","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_90","doi-asserted-by":"crossref","unstructured":"Talukdar, S., Singha, P., Mahato, S., Pal, S., Liou, Y.-A., and Rahman, A. (2020). Land-use land-cover classification by machine learning classifiers for satellite observations\u2014A review. Remote Sens., 12.","DOI":"10.3390\/rs12071135"},{"key":"ref_91","doi-asserted-by":"crossref","first-page":"111563","DOI":"10.1016\/j.rse.2019.111563","article-title":"Integrating Google Earth imagery with Landsat data to improve 30-m resolution land cover mapping","volume":"237","author":"Li","year":"2020","journal-title":"Remote Sens. Environ."},{"key":"ref_92","doi-asserted-by":"crossref","unstructured":"Huang, W., Li, W., Xu, J., Ma, X., Li, C., and Liu, C. (2022). Hyperspectral Monitoring Driven by Machine Learning Methods for Grassland Above-Ground Biomass. Remote Sens., 14.","DOI":"10.3390\/rs14092086"},{"key":"ref_93","doi-asserted-by":"crossref","unstructured":"Qi, S., Song, B., Liu, C., Gong, P., Luo, J., Zhang, M., and Xiong, T. (2022). Bamboo Forest Mapping in China Using the Dense Landsat 8 Image Archive and Google Earth Engine. Remote Sens., 14.","DOI":"10.3390\/rs14030762"},{"key":"ref_94","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_95","doi-asserted-by":"crossref","first-page":"185","DOI":"10.1016\/S0034-4257(01)00295-4","article-title":"Status of land cover classification accuracy assessment","volume":"80","author":"Foody","year":"2002","journal-title":"Remote Sens. Environ."},{"key":"ref_96","first-page":"223","article-title":"A coefficient of agreement as a measure of thematic classification accuracy","volume":"52","author":"Rosenfield","year":"1986","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_97","doi-asserted-by":"crossref","first-page":"77","DOI":"10.1016\/S0034-4257(97)00083-7","article-title":"Selecting and interpreting measures of thematic classification accuracy","volume":"62","author":"Stehman","year":"1997","journal-title":"Remote Sens. Environ."},{"key":"ref_98","doi-asserted-by":"crossref","unstructured":"Mostafa, E., Li, X., Sadek, M., and Dossou, J.F. (2021). Monitoring and Forecasting of Urban Expansion Using Machine Learning-Based Techniques and Remotely Sensed Data: A Case Study of Gharbia Governorate, Egypt. Remote Sens., 13.","DOI":"10.3390\/rs13224498"},{"key":"ref_99","doi-asserted-by":"crossref","first-page":"42","DOI":"10.1016\/j.rse.2014.02.015","article-title":"Good practices for estimating area and assessing accuracy of land change","volume":"148","author":"Olofsson","year":"2014","journal-title":"Remote Sens. Environ."},{"key":"ref_100","first-page":"102607","article-title":"Assessing the effects of irrigated agricultural expansions on Lake Urmia using multi-decadal Landsat imagery and a sample migration technique within Google Earth Engine","volume":"105","author":"Naboureh","year":"2021","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_101","doi-asserted-by":"crossref","first-page":"49","DOI":"10.1016\/j.techfore.2017.06.032","article-title":"A review of the ecosystem concept\u2014Towards coherent ecosystem design","volume":"136","author":"Tsujimoto","year":"2018","journal-title":"Technol. Forecast. Soc. Change"},{"key":"ref_102","doi-asserted-by":"crossref","first-page":"284","DOI":"10.2307\/1930070","article-title":"The use and abuse of vegetational concepts and terms","volume":"16","author":"Tansley","year":"1935","journal-title":"Ecology"},{"key":"ref_103","doi-asserted-by":"crossref","first-page":"1706","DOI":"10.1111\/brv.12636","article-title":"Effective ecosystem monitoring requires a multi-scaled approach","volume":"95","author":"Sparrow","year":"2020","journal-title":"Biol. Rev."},{"key":"ref_104","doi-asserted-by":"crossref","first-page":"92","DOI":"10.1016\/j.rse.2011.06.027","article-title":"Monitoring gradual ecosystem change using Landsat time series analyses: Case studies in selected forest and rangeland ecosystems","volume":"122","author":"Vogelmann","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_105","unstructured":"Odum, E.P., and Barrett, G.W. (1971). Fundamentals of Ecology, Saunders."},{"key":"ref_106","unstructured":"Luken, J.O. (1990). Directing Ecological Succession, Springer Science & Business Media."},{"key":"ref_107","doi-asserted-by":"crossref","first-page":"512","DOI":"10.1002\/ldr.4166","article-title":"Potential of vegetation and woodland cover recovery during primary and secondary succession, a global quantitative review","volume":"33","author":"Coradini","year":"2022","journal-title":"Land Degrad. Dev."},{"key":"ref_108","doi-asserted-by":"crossref","first-page":"503","DOI":"10.1111\/1365-2745.13132","article-title":"Ecological succession in a changing world","volume":"107","author":"Chang","year":"2019","journal-title":"J. Ecol."},{"key":"ref_109","doi-asserted-by":"crossref","first-page":"509","DOI":"10.1890\/09-1552.1","article-title":"Understanding ecosystem retrogression","volume":"80","author":"Peltzer","year":"2010","journal-title":"Ecol. Monogr."},{"key":"ref_110","doi-asserted-by":"crossref","first-page":"2214","DOI":"10.1360\/04yd0128","article-title":"Land cover changes based on plant successions: Deforestation, rehabilitation and degeneration of forest in the upper Dadu River watershed","volume":"48","author":"Yan","year":"2005","journal-title":"Sci. China Ser. D Earth Sci."},{"key":"ref_111","doi-asserted-by":"crossref","first-page":"1395","DOI":"10.1007\/s10980-007-9119-1","article-title":"Observing succession on aspen-dominated landscapes using a remote sensing-ecosystem approach","volume":"22","author":"Bergen","year":"2007","journal-title":"Landsc. Ecol."},{"key":"ref_112","doi-asserted-by":"crossref","first-page":"53","DOI":"10.1016\/j.jaridenv.2012.12.001","article-title":"Ecological succession and land use changes in a lake retreat area (Main Ethiopian Rift Valley)","volume":"91","author":"Temesgen","year":"2013","journal-title":"J. Arid. Environ."},{"key":"ref_113","doi-asserted-by":"crossref","first-page":"1189","DOI":"10.1214\/aos\/1013203451","article-title":"Greedy function approximation: A gradient boosting machine","volume":"29","author":"Friedman","year":"2001","journal-title":"Ann. Stat."},{"key":"ref_114","doi-asserted-by":"crossref","first-page":"3178","DOI":"10.1890\/0012-9658(2000)081[3178:CARTAP]2.0.CO;2","article-title":"Classification and regression trees: A powerful yet simple technique for ecological data analysis","volume":"81","author":"Fabricius","year":"2000","journal-title":"Ecology"},{"key":"ref_115","doi-asserted-by":"crossref","first-page":"273","DOI":"10.1007\/BF00994018","article-title":"Support-vector networks","volume":"20","author":"Cortes","year":"1995","journal-title":"Mach. Learn."},{"key":"ref_116","doi-asserted-by":"crossref","unstructured":"Chen, Z., Kang, Y., Sun, Z., Wu, F., and Zhang, Q. (2022). Extraction of Photovoltaic Plants Using Machine Learning Methods: A Case Study of the Pilot Energy City of Golmud, China. Remote Sens., 14.","DOI":"10.3390\/rs14112697"},{"key":"ref_117","doi-asserted-by":"crossref","first-page":"2046","DOI":"10.3390\/rs70202046","article-title":"Classification of Herbaceous Vegetation Using Airborne Hyperspectral Imagery","volume":"7","author":"Burai","year":"2015","journal-title":"Remote Sens."},{"key":"ref_118","doi-asserted-by":"crossref","unstructured":"Ghayour, L., Neshat, A., Paryani, S., Shahabi, H., Shirzadi, A., Chen, W., Al-Ansari, N., Geertsema, M., Pourmehdi Amiri, M., and Gholamnia, M. (2021). Performance Evaluation of Sentinel-2 and Landsat 8 OLI Data for Land Cover\/Use Classification Using a Comparison between Machine Learning Algorithms. Remote Sens., 13.","DOI":"10.3390\/rs13071349"},{"key":"ref_119","doi-asserted-by":"crossref","unstructured":"Saboori, M., Homayouni, S., Shah-Hosseini, R., and Zhang, Y. (2022). Optimum Feature and Classifier Selection for Accurate Urban Land Use\/Cover Mapping from Very High Resolution Satellite Imagery. Remote Sens., 14.","DOI":"10.3390\/rs14092097"},{"key":"ref_120","doi-asserted-by":"crossref","first-page":"115","DOI":"10.1016\/j.rse.2019.01.018","article-title":"Evaluation of Sentinel-2 time-series for mapping floodplain grassland plant communities","volume":"223","author":"Rapinel","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_121","doi-asserted-by":"crossref","unstructured":"Xiong, K., Adhikari, B.R., Stamatopoulos, C.A., Zhan, Y., Wu, S., Dong, Z., and Di, B. (2020). Comparison of Different Machine Learning Methods for Debris Flow Susceptibility Mapping: A Case Study in the Sichuan Province, China. Remote Sens., 12.","DOI":"10.3390\/rs12020295"},{"key":"ref_122","doi-asserted-by":"crossref","unstructured":"Liu, S., Qi, Z., Li, X., and Yeh, A.G.-O. (2019). Integration of convolutional neural networks and object-based post-classification refinement for land use and land cover mapping with optical and SAR data. Remote Sens., 11.","DOI":"10.3390\/rs11060690"},{"key":"ref_123","doi-asserted-by":"crossref","first-page":"1558","DOI":"10.1016\/j.foreco.2010.07.004","article-title":"When and where to actively restore ecosystems?","volume":"261","author":"Holl","year":"2011","journal-title":"For. Ecol. Manag."},{"key":"ref_124","doi-asserted-by":"crossref","first-page":"1455","DOI":"10.1126\/science.aaf2295","article-title":"Improvements in ecosystem services from investments in natural capital","volume":"352","author":"Ouyang","year":"2016","journal-title":"Science"},{"key":"ref_125","doi-asserted-by":"crossref","first-page":"114303","DOI":"10.1016\/j.jenvman.2021.114303","article-title":"Assessing effects of the Returning Farmland to Forest Program on vegetation cover changes at multiple spatial scales: The case of northwest Yunnan, China","volume":"304","author":"Li","year":"2022","journal-title":"J. Environ. Manag."},{"key":"ref_126","doi-asserted-by":"crossref","first-page":"112148","DOI":"10.1016\/j.rse.2020.112148","article-title":"High-resolution wall-to-wall land-cover mapping and land change assessment for Australia from 1985 to 2015","volume":"252","author":"Hadjikakou","year":"2021","journal-title":"Remote Sens. Environ."},{"key":"ref_127","doi-asserted-by":"crossref","unstructured":"Li, W., Xue, P., Liu, C., Yan, H., Zhu, G., and Cao, Y. (2020). Monitoring and Landscape Dynamic Analysis of Alpine Wetland Area Based on Multiple Algorithms: A Case Study of Zoige Plateau. Sensors, 20.","DOI":"10.3390\/s20247315"},{"key":"ref_128","doi-asserted-by":"crossref","first-page":"109","DOI":"10.1016\/j.isprsjprs.2020.01.001","article-title":"Examining earliest identifiable timing of crops using all available Sentinel 1\/2 imagery and Google Earth Engine","volume":"161","author":"You","year":"2020","journal-title":"ISPRS J. Photogramm. Remote Sens."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/21\/5361\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T01:03:09Z","timestamp":1760144589000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/21\/5361"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,10,26]]},"references-count":128,"journal-issue":{"issue":"21","published-online":{"date-parts":[[2022,11]]}},"alternative-id":["rs14215361"],"URL":"https:\/\/doi.org\/10.3390\/rs14215361","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,10,26]]}}}