{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,23]],"date-time":"2026-03-23T13:26:27Z","timestamp":1774272387012,"version":"3.50.1"},"reference-count":46,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2024,6,15]],"date-time":"2024-06-15T00:00:00Z","timestamp":1718409600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100012166","name":"National Key R&amp;D Program of China","doi-asserted-by":"publisher","award":["2022YFC3204400"],"award-info":[{"award-number":["2022YFC3204400"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Key Laboratory of Land and Sea Ecological Governance and Systematic Regulation","award":["2022YFC3204400"],"award-info":[{"award-number":["2022YFC3204400"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Inland aquaculture ponds, as an important land use type, have brought great economic benefits to local people but at the same time have caused many environmental problems threatening regional ecology security. Therefore, understanding the spatiotemporal pattern of aquaculture ponds and its potential influence on water quality is vital for the sustainable development of inland lakes. In this study, based on Landsat5\/8 images, three types of land features, namely spectral features, index features, and texture features, and five machine learning algorithms, namely random forest (RF), extreme gradient boosting (XGBoost), artificial neural network (ANN), k-nearest neighbor (KNN), and Gaussian naive Bayes (GNB), were combined to identify aquaculture ponds and some other primary land use types around a typical inland lake of China. The results demonstrated that the XGBoost algorithm that integrated the three features performed the best among all groups of the five machine learning algorithms and the three features, with an overall accuracy of up to 96.15%. In particular, the texture features provided additional useful information besides the spectral features to allow more accurately separation of aquaculture ponds from other land use types and thus improve the land use mapping ability in complex inland lakes. Next, this study examined the tendency of aquaculture ponds and found a segmented increase mode, namely sharp increase during 1984\u20132003 and then slow elevation since 2003. Further positive correlation detected between the area of aquaculture ponds and the phytoplankton population dynamics suggest a likely influence of aquaculture activity on the lake water quality. This study provides an important scientific basis for the sustainable management and ecological protection of inland lakes.<\/jats:p>","DOI":"10.3390\/rs16122168","type":"journal-article","created":{"date-parts":[[2024,6,17]],"date-time":"2024-06-17T04:48:12Z","timestamp":1718599692000},"page":"2168","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Aquaculture Ponds Identification Based on Multi-Feature Combination Strategy and Machine Learning from Landsat-5\/8 in a Typical Inland Lake of China"],"prefix":"10.3390","volume":"16","author":[{"given":"Gang","family":"Xie","sequence":"first","affiliation":[{"name":"Key Laboratory of Land and Sea Ecological Governance and Systematic Regulation, Shandong Academy for Environmental Planning, Jinan 250101, China"}]},{"given":"Xiaohui","family":"Bai","sequence":"additional","affiliation":[{"name":"Academician Workstation for Big Data in Ecology and Environment, Environment Research Institute, Shandong University, Qingdao 266003, China"}]},{"given":"Yanbo","family":"Peng","sequence":"additional","affiliation":[{"name":"Key Laboratory of Land and Sea Ecological Governance and Systematic Regulation, Shandong Academy for Environmental Planning, Jinan 250101, China"}]},{"given":"Yi","family":"Li","sequence":"additional","affiliation":[{"name":"Key Laboratory of Land and Sea Ecological Governance and Systematic Regulation, Shandong Academy for Environmental Planning, Jinan 250101, China"}]},{"given":"Chuanxing","family":"Zhang","sequence":"additional","affiliation":[{"name":"Key Laboratory of Land and Sea Ecological Governance and Systematic Regulation, Shandong Academy for Environmental Planning, Jinan 250101, China"}]},{"given":"Yang","family":"Liu","sequence":"additional","affiliation":[{"name":"Key Laboratory of Land and Sea Ecological Governance and Systematic Regulation, Shandong Academy for Environmental Planning, Jinan 250101, China"}]},{"given":"Jinhui","family":"Liang","sequence":"additional","affiliation":[{"name":"Key Laboratory of Land and Sea Ecological Governance and Systematic Regulation, Shandong Academy for Environmental Planning, Jinan 250101, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7194-8742","authenticated-orcid":false,"given":"Lei","family":"Fang","sequence":"additional","affiliation":[{"name":"Academician Workstation for Big Data in Ecology and Environment, Environment Research Institute, Shandong University, Qingdao 266003, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6912-435X","authenticated-orcid":false,"given":"Jinyue","family":"Chen","sequence":"additional","affiliation":[{"name":"Academician Workstation for Big Data in Ecology and Environment, Environment Research Institute, Shandong University, Qingdao 266003, China"}]},{"given":"Jilin","family":"Men","sequence":"additional","affiliation":[{"name":"Academician Workstation for Big Data in Ecology and Environment, Environment Research Institute, Shandong University, Qingdao 266003, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0911-7312","authenticated-orcid":false,"given":"Xinfeng","family":"Wang","sequence":"additional","affiliation":[{"name":"Academician Workstation for Big Data in Ecology and Environment, Environment Research Institute, Shandong University, Qingdao 266003, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3976-1056","authenticated-orcid":false,"given":"Guoqiang","family":"Wang","sequence":"additional","affiliation":[{"name":"Academician Workstation for Big Data in Ecology and Environment, Environment Research Institute, Shandong University, Qingdao 266003, China"},{"name":"Center for Geodata and Analysis, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China"}]},{"given":"Qiao","family":"Wang","sequence":"additional","affiliation":[{"name":"Academician Workstation for Big Data in Ecology and Environment, Environment Research Institute, Shandong University, Qingdao 266003, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7806-6179","authenticated-orcid":false,"given":"Shilong","family":"Ren","sequence":"additional","affiliation":[{"name":"Academician Workstation for Big Data in Ecology and Environment, Environment Research Institute, Shandong University, Qingdao 266003, China"}]}],"member":"1968","published-online":{"date-parts":[[2024,6,15]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Dehong, D., Yinfang, S., Lin, S., and Genxia, W. (2012, January 1\u20133). Remote Sensing Technology\u2019s Applied Research and Development Direction in Land-Use and Land-Cover Change (LUCC). Proceedings of the 2012 2nd International Conference on Remote Sensing, Environment and Transportation Engineering, Nanjing, China.","DOI":"10.1109\/RSETE.2012.6260681"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"113","DOI":"10.1016\/j.ecoser.2017.08.013","article-title":"Linking land use change, ecosystem services and human well-being: A case study of the Manas River Basin of Xinjiang, China","volume":"27","author":"Wang","year":"2017","journal-title":"Ecosyst. Serv."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"133","DOI":"10.1126\/science.1260149","article-title":"China\u2019s aquaculture and the world\u2019s wild fisheries","volume":"347","author":"Cao","year":"2015","journal-title":"Science"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"102422","DOI":"10.1016\/j.foodpol.2023.102422","article-title":"A global view of aquaculture policy","volume":"116","author":"Naylor","year":"2023","journal-title":"Food Policy"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"106183","DOI":"10.1016\/j.resconrec.2022.106183","article-title":"Environmental sustainability and footprints of global aquaculture","volume":"180","author":"Jiang","year":"2022","journal-title":"Resour. Conserv. Recycl."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Luo, J., Pu, R., Ma, R., Wang, X., Lai, X., Mao, Z., Zhang, L., Peng, Z., and Sun, Z. (2020). Mapping long-term spatiotemporal dynamics of pen aquaculture in a shallow lake: Less aquaculture coming along better water quality. Remote Sens., 12.","DOI":"10.3390\/rs12111866"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"739474","DOI":"10.1016\/j.aquaculture.2023.739474","article-title":"Policy-driven opposite changes of coastal aquaculture ponds between China and Vietnam: Evidence from Sentinel-1 images","volume":"571","author":"Sun","year":"2023","journal-title":"Aquaculture"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"27","DOI":"10.1016\/j.isprsjprs.2016.12.008","article-title":"Multi-source remotely sensed data fusion for improving land cover classification","volume":"124","author":"Chen","year":"2017","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Li, R., Gao, X., Shi, F., and Zhang, H. (2023). Scale Effect of Land Cover Classification from Multi-Resolution Satellite Remote Sensing Data. Sensors, 23.","DOI":"10.20944\/preprints202305.0371.v1"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"23","DOI":"10.1016\/j.rse.2013.08.029","article-title":"Automated Water Extraction Index: A new technique for surface water mapping using Landsat imagery","volume":"140","author":"Feyisa","year":"2014","journal-title":"Remote Sens. Environ."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Huang, D.-M., Wei, C.-T., Yu, J.-C., and Wang, J.-L. (2015, January 23\u201324). A method of detecting land use change of remote sensing images based on texture features and DEM. Proceedings of the International Conference on Intelligent Earth Observing and Applications, Guilin, China.","DOI":"10.1117\/12.2214637"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Wang, H., Zhao, H., and Li, W. (2014, January 19\u201321). Land-use Classification of Zhanghe River Basin Using the Maximum Likelihood and Decision Tree Method. Proceedings of the 11th International Conference on Fuzzy Systems and Knowledge Discovery (FSKD), Xiamen, China.","DOI":"10.1109\/FSKD.2014.6980854"},{"key":"ref_13","unstructured":"Li, Y., and Wu, H. (2012, January 1\u20132). A Clustering Method Based on K-Means Algorithm. Proceedings of the International Conference on Solid State Devices and Materials Science (SSDMS), Macao, China."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"2333","DOI":"10.1109\/JSTARS.2016.2557584","article-title":"A Hyperheuristic Approach for Unsupervised Land-Cover Classification","volume":"9","author":"Papa","year":"2016","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"648","DOI":"10.1016\/j.rse.2017.09.035","article-title":"Effect of classifier selection, reference sample size, reference class distribution and scene heterogeneity in per-pixel classification accuracy using 26 Landsat sites","volume":"204","author":"Heydari","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"083636","DOI":"10.1117\/1.JRS.8.083636","article-title":"Enhanced land use\/cover classification using support vector machines and fuzzy k-means clustering algorithms","volume":"8","author":"He","year":"2014","journal-title":"J. Appl. Remote Sens."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"2663","DOI":"10.1080\/01431161.2020.1857877","article-title":"Parametric study of convolutional neural network based remote sensing image classification","volume":"42","author":"Shakya","year":"2021","journal-title":"Int. J. Remote Sens."},{"key":"ref_18","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_19","doi-asserted-by":"crossref","unstructured":"Chen, T., and Guestrin, C. (2016, January 13\u201317). Xgboost: A scalable tree boosting system. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Francisco, CA, USA.","DOI":"10.1145\/2939672.2939785"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"823","DOI":"10.1080\/01431160600746456","article-title":"A survey of image classification methods and techniques for improving classification performance","volume":"28","author":"Lu","year":"2007","journal-title":"Int. J. Remote Sens."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"2297","DOI":"10.1109\/TGRS.2009.2039484","article-title":"Feature Selection for Classification of Hyperspectral Data by SVM","volume":"48","author":"Pal","year":"2010","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"734666","DOI":"10.1016\/j.aquaculture.2019.734666","article-title":"Mapping national-scale aquaculture ponds based on the Google Earth Engine in the Chinese coastal zone","volume":"520","author":"Duan","year":"2020","journal-title":"Aquaculture"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Zeng, Z., Wang, D., Tan, W., Yu, G., You, J., Lv, B., and Wu, Z. (2020). RCSANet: A full convolutional network for extracting inland aquaculture ponds from high-spatial-resolution images. Remote Sens., 13.","DOI":"10.3390\/rs13010092"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"737078","DOI":"10.1016\/j.aquaculture.2021.737078","article-title":"Patterns of phytoplankton community structure and diversity in aquaculture ponds, Henan, China","volume":"544","author":"Zhang","year":"2021","journal-title":"Aquaculture"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"104304","DOI":"10.1016\/j.jconhyd.2024.104304","article-title":"Remote sensing retrieval and driving analysis of phytoplankton density in the large storage freshwater lake: A study based on random forest and Landsat-8 OLI","volume":"261","author":"Wang","year":"2024","journal-title":"J. Contam. Hydrol."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Gu, Z., Zhang, Z., Yang, J., and Wang, L. (2022). Quantifying the influences of driving factors on vegetation EVI changes using structural equation model: A case study in Anhui province, China. Remote Sens., 14.","DOI":"10.3390\/rs14174203"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"106081","DOI":"10.1016\/j.agwat.2020.106081","article-title":"Remote sensing for estimating and mapping single and basal crop coefficientes: A review on spectral vegetation indices approaches","volume":"233","author":"Calera","year":"2020","journal-title":"Agric. Water Manag."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"2645","DOI":"10.1016\/j.procs.2020.04.287","article-title":"Analysis of surface water resources using Sentinel-2 imagery","volume":"171","author":"Bhangale","year":"2020","journal-title":"Procedia Comput. Sci."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"59","DOI":"10.1016\/j.isprsjprs.2020.12.003","article-title":"A novel surface water index using local background information for long term and large-scale Landsat images","volume":"172","author":"Li","year":"2021","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"107230","DOI":"10.1016\/j.compag.2022.107230","article-title":"Mapping the soil types combining multi-temporal remote sensing data with texture features","volume":"200","author":"Duan","year":"2022","journal-title":"Comput. Electron. Agric."},{"key":"ref_31","first-page":"102475","article-title":"Identifying core driving factors of urban land use change from global land cover products and POI data using the random forest method","volume":"103","author":"Wu","year":"2021","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"112105","DOI":"10.1016\/j.rse.2020.112105","article-title":"Improving land cover classification in an urbanized coastal area by random forests: The role of variable selection","volume":"251","author":"Zhang","year":"2020","journal-title":"Remote Sens. Environ."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1080\/15481603.2019.1650447","article-title":"Land cover and land use classification performance of machine learning algorithms in a boreal landscape using Sentinel-2 data","volume":"57","author":"Abdi","year":"2020","journal-title":"GIScience Remote Sens."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"119376","DOI":"10.1016\/j.jenvman.2023.119376","article-title":"Patterns and drivers of carbon stock change in ecological restoration regions: A case study of upper Yangtze River Basin, China","volume":"348","author":"Quan","year":"2023","journal-title":"J. Environ. Manag."},{"key":"ref_35","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_36","doi-asserted-by":"crossref","unstructured":"Duran, Z., Ozcan, K., and Atik, M.E. (2021). Classification of photogrammetric and airborne lidar point clouds using machine learning algorithms. Drones, 5.","DOI":"10.3390\/drones5040104"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"130446","DOI":"10.1016\/j.jhydrol.2023.130446","article-title":"HLEL: A wetland classification algorithm with self-learning capability, taking the Sanjiang Nature Reserve I as an example","volume":"627","author":"Jiang","year":"2023","journal-title":"J. Hydrol."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"217","DOI":"10.1080\/01431160412331269698","article-title":"Random forest classifier for remote sensing classification","volume":"26","author":"Pal","year":"2005","journal-title":"Int. J. Remote Sens."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Usman, M., Ejaz, M., Nichol, J.E., Farid, M.S., Abbas, S., and Khan, M.H. (2023). A Comparison of Machine Learning Models for Mapping Tree Species Using WorldView-2 Imagery in the Agroforestry Landscape of West Africa. ISPRS Int. J. Geo-Inf., 12.","DOI":"10.3390\/ijgi12040142"},{"key":"ref_40","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_41","doi-asserted-by":"crossref","first-page":"116554","DOI":"10.1016\/j.eswa.2022.116554","article-title":"Complex features extraction with deep learning model for the detection of COVID19 from CT scan images using ensemble based machine learning approach","volume":"195","author":"Islam","year":"2022","journal-title":"Expert Syst. Appl."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"3193","DOI":"10.1007\/s00382-022-06252-x","article-title":"The area prediction of western North Pacific Subtropical High in summer based on Gaussian Naive Bayes","volume":"59","author":"Li","year":"2022","journal-title":"Clim. Dyn."},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Mao, W., Lu, D., Hou, L., Liu, X., and Yue, W. (2020). Comparison of machine-learning methods for urban land-use mapping in Hangzhou city, China. Remote Sens., 12.","DOI":"10.3390\/rs12172817"},{"key":"ref_44","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_45","doi-asserted-by":"crossref","first-page":"012166","DOI":"10.1088\/1757-899X\/745\/1\/012166","article-title":"Accuracy assessment of supervised classification methods for extraction land use maps using remote sensing and GIS techniques","volume":"745","author":"Abbas","year":"2020","journal-title":"IOP Conf. Ser. Mater. Sci. Eng."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"105348","DOI":"10.1016\/j.ocecoaman.2020.105348","article-title":"Automatic extraction of aquaculture ponds based on Google Earth Engine","volume":"198","author":"Xia","year":"2020","journal-title":"Ocean. Coast. Manag."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/12\/2168\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T14:59:11Z","timestamp":1760108351000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/12\/2168"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,6,15]]},"references-count":46,"journal-issue":{"issue":"12","published-online":{"date-parts":[[2024,6]]}},"alternative-id":["rs16122168"],"URL":"https:\/\/doi.org\/10.3390\/rs16122168","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,6,15]]}}}