{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,9]],"date-time":"2026-04-09T14:23:02Z","timestamp":1775744582923,"version":"3.50.1"},"reference-count":66,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2022,6,2]],"date-time":"2022-06-02T00:00:00Z","timestamp":1654128000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Key Laboratory of Desert and Desertification","award":["KLDD-2020\u2013018"],"award-info":[{"award-number":["KLDD-2020\u2013018"]}]},{"name":"Key Laboratory of Desert and Desertification","award":["2020BBF02003"],"award-info":[{"award-number":["2020BBF02003"]}]},{"name":"Key Laboratory of Desert and Desertification","award":["2019QZKK0305"],"award-info":[{"award-number":["2019QZKK0305"]}]},{"name":"Key Laboratory of Desert and Desertification","award":["2016YFC0500902"],"award-info":[{"award-number":["2016YFC0500902"]}]},{"name":"Key Research and Development Program of Ningxia Hui Autonomous Region","award":["KLDD-2020\u2013018"],"award-info":[{"award-number":["KLDD-2020\u2013018"]}]},{"name":"Key Research and Development Program of Ningxia Hui Autonomous Region","award":["2020BBF02003"],"award-info":[{"award-number":["2020BBF02003"]}]},{"name":"Key Research and Development Program of Ningxia Hui Autonomous Region","award":["2019QZKK0305"],"award-info":[{"award-number":["2019QZKK0305"]}]},{"name":"Key Research and Development Program of Ningxia Hui Autonomous Region","award":["2016YFC0500902"],"award-info":[{"award-number":["2016YFC0500902"]}]},{"name":"Second Tibetan Plateau Scientific Expedition and Research Program","award":["KLDD-2020\u2013018"],"award-info":[{"award-number":["KLDD-2020\u2013018"]}]},{"name":"Second Tibetan Plateau Scientific Expedition and Research Program","award":["2020BBF02003"],"award-info":[{"award-number":["2020BBF02003"]}]},{"name":"Second Tibetan Plateau Scientific Expedition and Research Program","award":["2019QZKK0305"],"award-info":[{"award-number":["2019QZKK0305"]}]},{"name":"Second Tibetan Plateau Scientific Expedition and Research Program","award":["2016YFC0500902"],"award-info":[{"award-number":["2016YFC0500902"]}]},{"name":"National Key Research and Development Program of China","award":["KLDD-2020\u2013018"],"award-info":[{"award-number":["KLDD-2020\u2013018"]}]},{"name":"National Key Research and Development Program of China","award":["2020BBF02003"],"award-info":[{"award-number":["2020BBF02003"]}]},{"name":"National Key Research and Development Program of China","award":["2019QZKK0305"],"award-info":[{"award-number":["2019QZKK0305"]}]},{"name":"National Key Research and Development Program of China","award":["2016YFC0500902"],"award-info":[{"award-number":["2016YFC0500902"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Mu Us Sandy Land is a typical semi-arid vulnerable ecological zone, characterized by vegetation degradation and severe desertification. Effectively identifying desertification changes has been a topical environmental issue in China. However, most previous studies have used a single method or remote sensing index to monitor desertification, and lacked an efficient and high-precision monitoring system. In this study, an optimal monitoring scheme that considers multiple indicators combination and different machine learning methods (Classification and Regression Tree-Decision Tree, CART-DT; Random Forest, RF; Convolutional Neural Networks, CNN) was developed and used to analyze the spatial\u2013temporal patterns of desertification from 2000 to 2018 in Mu Us Sandy Land. The results showed that: (a) The random forest model performed best for monitoring desertification based on medium and low-resolution remote sensing images, and the four-index combination (Albedo, NDVI, LST and TGSI) obtained the highest classification accuracy (OA = 87.67%) in Mu Us Sandy Land. Surprisingly, the model accuracy of the three-index combination (NDVI, LST and TGSI) (OA = 85.74%) is comparable to the four-index combination. (b) The TGSI index used to characterize soil information performs well, while the LST is not conducive to the extraction of desertified land in several desertification monitoring indicators. (c) Since 2000, the area of extremely severe desertified land has shown a reversal trend; however, there is significant interannual fluctuation in the total and light desertification land area affected by extreme climate. This research provides a novel approach and a valuable reference for monitoring the evolution of desertification in regional studies, and the results improve the research system of desertification and provide a data basis for desertification cause analysis and prevention.<\/jats:p>","DOI":"10.3390\/rs14112663","type":"journal-article","created":{"date-parts":[[2022,6,3]],"date-time":"2022-06-03T08:01:18Z","timestamp":1654243278000},"page":"2663","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":49,"title":["Monitoring Desertification Using Machine-Learning Techniques with Multiple Indicators Derived from MODIS Images in Mu Us Sandy Land, China"],"prefix":"10.3390","volume":"14","author":[{"given":"Kun","family":"Feng","sequence":"first","affiliation":[{"name":"Key Laboratory of Desert and Desertification, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou 730000, China"}]},{"given":"Tao","family":"Wang","sequence":"additional","affiliation":[{"name":"Key Laboratory of Desert and Desertification, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou 730000, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8487-3260","authenticated-orcid":false,"given":"Shulin","family":"Liu","sequence":"additional","affiliation":[{"name":"Key Laboratory of Desert and Desertification, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou 730000, China"}]},{"given":"Wenping","family":"Kang","sequence":"additional","affiliation":[{"name":"Key Laboratory of Desert and Desertification, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou 730000, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6064-6995","authenticated-orcid":false,"given":"Xiang","family":"Chen","sequence":"additional","affiliation":[{"name":"College of Geography and Environmental Science, Northwest Normal University, Lanzhou 730070, China"}]},{"given":"Zichen","family":"Guo","sequence":"additional","affiliation":[{"name":"Key Laboratory of Desert and Desertification, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou 730000, China"}]},{"given":"Ying","family":"Zhi","sequence":"additional","affiliation":[{"name":"Key Laboratory of Desert and Desertification, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou 730000, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100049, China"}]}],"member":"1968","published-online":{"date-parts":[[2022,6,2]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1328","DOI":"10.1017\/S0020782900026711","article-title":"United Nations: Convention to combat desertification in those countries experiencing serious drought and\/or desertification, particularly in Africa","volume":"33","author":"Ma","year":"1994","journal-title":"Int. Legal Mater."},{"key":"ref_2","unstructured":"UNEP (1992). Status of Desertification and Implementation of the United Nations Plan of Action to Combat Desertification: United Nations Environmental Program, UNEP."},{"key":"ref_3","first-page":"169","article-title":"Regional desertification: A global synthesis. Global Planet","volume":"64","author":"Hellden","year":"2008","journal-title":"Change"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1060","DOI":"10.1002\/ldr.3746","article-title":"Multi-scenario simulation of desertification in North China for 2030","volume":"32","author":"Xu","year":"2021","journal-title":"Land Degrad. Dev."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"182","DOI":"10.1016\/j.envsci.2018.11.017","article-title":"Land degradation neutrality: The science-policy interface from the UNCCD to national implementation","volume":"92","author":"Chasek","year":"2019","journal-title":"Environ. Sci. Policy"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"118","DOI":"10.1002\/ldr.2190","article-title":"Combating aeolian desertification in northern China","volume":"26","author":"Wang","year":"2015","journal-title":"Land Degrad. Dev."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"213","DOI":"10.1038\/nature06111","article-title":"Spatial vegetation patterns and imminent desertification in Mediterranean arid ecosystems","volume":"449","author":"Rietkerk","year":"2007","journal-title":"Nature"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"555","DOI":"10.1016\/j.geomorph.2008.07.018","article-title":"Late Holocene dune activity in the Eastern Platte River Valley, Nebraska","volume":"103","author":"Hanson","year":"2009","journal-title":"Geomorphology"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.agee.2019.01.001","article-title":"Wind erosion changes induced by different grazing intensities in the desert steppe, Northern China","volume":"274","author":"Du","year":"2019","journal-title":"Agric. Ecosyst. Environ."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"37","DOI":"10.1016\/j.catena.2011.08.003","article-title":"Temporal-spatial variability of desertification in an agro-pastoral transitional zone of northern Shaanxi Province, China","volume":"88","author":"Qi","year":"2012","journal-title":"Catena"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"104123","DOI":"10.1016\/j.catena.2019.104123","article-title":"Spatiotemporal changes in the Aeolian desertification of Hulunbuir Grassland and its driving factors in China during 1980\u20132015","volume":"182","author":"Na","year":"2019","journal-title":"Catena"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"2374","DOI":"10.1016\/j.scitotenv.2018.09.374","article-title":"Dynamic monitoring of aeolian desertification based on multiple indicators in Horqin Sandy Land, China","volume":"650","author":"Duan","year":"2019","journal-title":"Sci. Total Environ."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"1648","DOI":"10.1016\/j.scitotenv.2017.10.137","article-title":"Monitoring of aeolian desertification on the Qinghai-Tibet Plateau from the 1970s to 2015 using Landsat images","volume":"619","author":"Zhang","year":"2018","journal-title":"Sci. Total Environ."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"11","DOI":"10.1016\/j.catena.2014.07.004","article-title":"The dynamics of desertification in the farming-pastoral region of North China over the past 10 years and their relationship to climate change and human activity","volume":"123","author":"Xu","year":"2014","journal-title":"Catena"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"50","DOI":"10.1080\/17538940903506006","article-title":"Tracking desertification on the Mongolian steppe through NDVI and field-survey data","volume":"4","author":"Sternberg","year":"2011","journal-title":"Int. J. Digit. Earth"},{"key":"ref_16","first-page":"197","article-title":"Desertification trends in the Northeast of Brazil over the period 2000\u20132016","volume":"73","author":"Tomasella","year":"2018","journal-title":"Int. J. Appl. Earth Obs."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"105647","DOI":"10.1016\/j.ecolind.2019.105647","article-title":"Effects of climatic and grazing changes on desertification of alpine grasslands, Northern Tibet","volume":"107","author":"Sun","year":"2019","journal-title":"Ecol. Indic."},{"key":"ref_18","first-page":"1","article-title":"Monitoring landcover change and desertification processes in northern China and Mongolia using historical written sources and vegetation indices","volume":"2021","author":"Kempf","year":"2021","journal-title":"Clim. Past Discuss."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Wei, H., Wang, J., Cheng, K., Li, G., Ochir, A., Davaasuren, D., and Chonokhuu, S. (2018). Desertification Information Extraction Based on Feature Space Combinations on the Mongolian Plateau. Remote Sens., 10.","DOI":"10.3390\/rs10101614"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"1573","DOI":"10.1002\/ldr.3533","article-title":"Dynamic monitoring of desertification in Naiman Banner based on feature space models with typical surface parameters derived from LANDSAT images","volume":"31","author":"Guo","year":"2020","journal-title":"Land Degrad. Dev."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"581","DOI":"10.3390\/su9040581","article-title":"Correlation between Desertification and Environmental Variables Using Remote Sensing Techniques in Hogno Khaan, Mongolia","volume":"9","author":"Munkhnasan","year":"2017","journal-title":"Sustainability"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"1738","DOI":"10.3390\/s90301738","article-title":"Quantitative Assessment of Desertification Using Landsat Data on a Regional Scale\u2014A Case Study in the Ordos Plateau, China","volume":"9","author":"Xu","year":"2009","journal-title":"Sensors"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"1968","DOI":"10.1002\/ldr.3393","article-title":"A data-mining-based approach for aeolian desertification susceptibility assessment: A case-study from Northern China","volume":"30","author":"Yue","year":"2019","journal-title":"Land Degrad. Dev."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Fan, Z., Li, S., and Fang, H. (2020). Explicitly Identifying the Desertification Change in CMREC Area Based on Multisource Remote Data. Remote Sens., 12.","DOI":"10.3390\/rs12193170"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"107908","DOI":"10.1016\/j.ecolind.2021.107908","article-title":"Monitoring desertification in Mongolia based on Landsat images and Google Earth Engine from 1990 to 2020","volume":"129","author":"Meng","year":"2021","journal-title":"Ecol. Indic."},{"key":"ref_26","unstructured":"Welsink, A. (2020). Comparing Classification of Ghana\u2019s Complex Agroforestry Land Cover by a Random Forest and a Convolutional Neural Network with a Small Training Set. [Master\u2019s Thesis, Wageningen University]."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"309","DOI":"10.1007\/s10812-020-01001-6","article-title":"Desertification Glassland Classification and Three-Dimensional Convolution Neural Network Model for Identifying Desert Grassland Landforms with Unmanned Aerial Vehicle Hyperspectral Remote Sensing Images","volume":"87","author":"Pi","year":"2020","journal-title":"J. Appl. Spectrosc."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"101278","DOI":"10.1016\/j.ecoinf.2021.101278","article-title":"3D-CNN based UAV hyperspectral imagery for grassland degradation indicator ground object classification research","volume":"62","author":"Pi","year":"2021","journal-title":"Ecol. Inform."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"107386","DOI":"10.1016\/j.ecolind.2021.107386","article-title":"Path analysis model to identify and analyse the causes of aeolian desertification in Mu Us Sandy Land, China","volume":"124","author":"Feng","year":"2021","journal-title":"Ecol. Indic."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"845","DOI":"10.1007\/BF02885034","article-title":"Developing stages and causes of desertification in the Mu Us sandland","volume":"44","author":"Wu","year":"1999","journal-title":"Chin. Sci. Bull."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"560","DOI":"10.1016\/j.ecolind.2014.08.043","article-title":"Quantitative assessment of the individual contribution of climate and human factors to desertification in northwest China using net primary productivity as an indicator","volume":"48","author":"Zhou","year":"2015","journal-title":"Ecol. Indic."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"268","DOI":"10.1016\/j.jenvman.2012.12.040","article-title":"Effects of grassland restoration programs on ecosystems in arid and semiarid China","volume":"117","author":"Huang","year":"2013","journal-title":"J. Environ. Manag."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Guo, Q., Fu, B., Shi, P., Cudahy, T., Zhang, J., and Xu, H. (2017). Satellite monitoring the spatial-temporal dynamics of desertification in response to climate change and human activities across the Ordos Plateau, China. Remote Sens., 9.","DOI":"10.3390\/rs9060525"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"697","DOI":"10.1016\/j.jclepro.2016.09.011","article-title":"Dynamic analysis of ecological environment combined with land cover and NDVI changes and implications for sustainable urban\u2013rural development: The case of Mu Us Sandy Land, China","volume":"142","author":"Li","year":"2017","journal-title":"Clean. Prod."},{"key":"ref_35","unstructured":"FAO, and FAO\/IUSS Working Group WRB (2006). World Reference Base for Soil Resources 2006, FAO. World Soil Resources Reports."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"106228","DOI":"10.1016\/j.ecolind.2020.106228","article-title":"The rebound effects of recent vegetation restoration projects in Mu Us Sandy land of China","volume":"113","author":"Zhang","year":"2020","journal-title":"Ecol. Indic."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"177","DOI":"10.1016\/j.isprsjprs.2016.09.008","article-title":"A global study of NDVI difference among moderate-resolution satellite sensors","volume":"121","author":"Fan","year":"2016","journal-title":"ISPRS J. Photogramm."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Wu, X., Wen, J., Xiao, Q., You, D., Dou, B., Lin, X., and Hueni, A. (2018). Accuracy Assessment on MODIS (V006), GLASS and MuSyQ Land-Surface Albedo Products: A Case Study in the Heihe River Basin, China. Remote Sens., 10.","DOI":"10.3390\/rs10122045"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"36","DOI":"10.1016\/j.rse.2013.08.027","article-title":"New refinements and validation of the collection-6 MODIS land-surface temperature\/emissivity product","volume":"140","author":"Wan","year":"2014","journal-title":"Remote Sens. Environ."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"12","DOI":"10.1007\/s40333-017-0109-0","article-title":"Monitoring desertification processes in Mongolian Plateau using MODIS tasseled cap transformation and TGSI time series","volume":"10","author":"Liu","year":"2018","journal-title":"J. Arid Land."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"393","DOI":"10.1016\/j.rse.2016.09.002","article-title":"Bayesian MODIS NDVI back-prediction by intersensor calibration with AVHRR","volume":"186","author":"Liang","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Lin, X., Niu, J., Berndtsson, R., Yu, X., Zhang, L., and Chen, X. (2020). NDVI Dynamics and Its Response to Climate Change and Reforestation in Northern China. Remote Sens., 12.","DOI":"10.3390\/rs12244138"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"2411","DOI":"10.1080\/01431160600554363","article-title":"Development of topsoil grain size index for monitoring desertification in arid land using remote sensing","volume":"27","author":"Xiao","year":"2006","journal-title":"Int. J. Remote Sens."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"81","DOI":"10.1007\/BF00116251","article-title":"Induction of decision trees","volume":"1","author":"Quinlan","year":"1986","journal-title":"Mach. Learn."},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Larose, D.T., and Larose, C.D. (2014). Discovering Knowledge in Data: AnIntroduction to Data Mining, John Wiley & Sons.","DOI":"10.1002\/9781118874059"},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Lamrini, B. (2020). Contribution to Decision Tree Induction with Python: A Review. Data Mining-Methods, Applications and Systems, IntechOpen.","DOI":"10.5772\/intechopen.92438"},{"key":"ref_47","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_48","doi-asserted-by":"crossref","first-page":"2999","DOI":"10.1016\/j.rse.2008.02.011","article-title":"Evaluation of Random Forest and Adaboost tree-based ensemble classification and spectral band selection for ecotope mapping using airborne hyperspectral imagery","volume":"112","author":"Chan","year":"2008","journal-title":"Remote Sens. Environ."},{"key":"ref_49","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."},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Chen, X., Wang, T., Liu, S., Peng, F., Tsunekawa, A., Kang, W., Guo, Z., and Feng, K. (2019). A New Application of Random Forest Algorithm to Estimate Coverage of Moss-Dominated Biological Soil Crusts in Semi-Arid Mu Us Sandy Land, China. Remote Sens., 11.","DOI":"10.3390\/rs11111286"},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"232","DOI":"10.1016\/j.matcom.2020.04.031","article-title":"Application of the residue number system to reduce hardware costs of the convolutional neural network implementation","volume":"177","author":"Valueva","year":"2020","journal-title":"Math. Comput. Simulat."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"279","DOI":"10.1016\/j.neunet.2019.04.025","article-title":"Differential convolutional neural network","volume":"116","author":"Sarigul","year":"2019","journal-title":"Neural Netw."},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Albawi, S., Mohammed, T.A., and Al-Zawi, S. (2017, January 21\u201323). Understanding of a Convolutional Neural Network. Proceedings of the International Conference on Engineering and Technology, Antalya, Turkey.","DOI":"10.1109\/ICEngTechnol.2017.8308186"},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"611","DOI":"10.1007\/s13244-018-0639-9","article-title":"Convolutional neural networks: An overview and application in radiology","volume":"9","author":"Yamashita","year":"2018","journal-title":"Insights Imaging"},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"Guirado, E., Alcaraz-Segura, D., Cabello, J., Puertas-Ruiz, S., Herrera, F., and Tabik, S. (2020). Tree Cover Estimation in Global Drylands from Space Using Deep Learning. Remote Sens., 12.","DOI":"10.3390\/rs12030343"},{"key":"ref_56","unstructured":"Tong, Q., Shan, J., and Zhu, B. (2014, January 20\u201323). Correlating Analysis on Spatio-temporal Variation of LUCC and Water Resources Based on Remote Sensing Data. Proceedings of the 18th National Symposium on Remote Sensing of China, Wuhan, China."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"233","DOI":"10.1007\/s10980-005-1051-7","article-title":"The impact of misclassification in land use maps in the prediction of landscape dynamics","volume":"21","author":"Fang","year":"2006","journal-title":"Landscape Ecol."},{"key":"ref_58","doi-asserted-by":"crossref","unstructured":"Chen, Y., Dou, P., and Yang, X. (2017). Improving Land Use\/Cover Classification with a Multiple Classifier System Using AdaBoost Integration Technique. Remote Sens., 9.","DOI":"10.3390\/rs9101055"},{"key":"ref_59","first-page":"e00971","article-title":"Land use\/cover classification in an arid desert-oasis mosaic landscape of China using remote sensed imagery: Performance assessment of four machine learning algorithms","volume":"22","author":"Ge","year":"2020","journal-title":"Glob. Ecol. Conserv."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"37","DOI":"10.1016\/S0169-2046(00)00136-5","article-title":"Modeling the dynamics of landscape structure in Asia\u2019s emerging desakota regions: A case study in Shenzhen","volume":"53","author":"Sui","year":"2001","journal-title":"Urban Plan."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"275","DOI":"10.1007\/BF02837407","article-title":"The extreme dry\/wet events in northern China during recent 100 years","volume":"14","author":"Zhuguo","year":"2004","journal-title":"J. Geogr. Sci."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"6308","DOI":"10.1109\/JSTARS.2020.3026724","article-title":"Support Vector Machine Versus Random Forest for Remote Sensing Image Classification: A Meta-Analysis and Systematic Review","volume":"13","author":"Sheykhmousa","year":"2020","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_63","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."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"277","DOI":"10.1016\/j.isprsjprs.2017.06.001","article-title":"A review of supervised object-based land-cover image classification","volume":"130","author":"Ma","year":"2017","journal-title":"ISPRS J. Photogramm."},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"442","DOI":"10.1002\/eap.1660","article-title":"Exacerbated grassland degradation and desertification in Central Asia during 2000\u20132014","volume":"28","author":"Zhang","year":"2018","journal-title":"Ecol. Appl."},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"321","DOI":"10.1016\/j.catena.2016.06.023","article-title":"Landscape spatial patterns in the Maowusu (Mu Us) Sandy Land, northern China and their impact factors","volume":"145","author":"Liang","year":"2016","journal-title":"Catena"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/11\/2663\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T23:23:47Z","timestamp":1760138627000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/11\/2663"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,6,2]]},"references-count":66,"journal-issue":{"issue":"11","published-online":{"date-parts":[[2022,6]]}},"alternative-id":["rs14112663"],"URL":"https:\/\/doi.org\/10.3390\/rs14112663","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,6,2]]}}}