{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,24]],"date-time":"2026-06-24T21:31:34Z","timestamp":1782336694230,"version":"3.54.5"},"reference-count":97,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2023,2,20]],"date-time":"2023-02-20T00:00:00Z","timestamp":1676851200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["41971025"],"award-info":[{"award-number":["41971025"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["XDA19030500"],"award-info":[{"award-number":["XDA19030500"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100002367","name":"Chinese Academy of Sciences","doi-asserted-by":"publisher","award":["41971025"],"award-info":[{"award-number":["41971025"]}],"id":[{"id":"10.13039\/501100002367","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100002367","name":"Chinese Academy of Sciences","doi-asserted-by":"publisher","award":["XDA19030500"],"award-info":[{"award-number":["XDA19030500"]}],"id":[{"id":"10.13039\/501100002367","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD)","award":["41971025"],"award-info":[{"award-number":["41971025"]}]},{"name":"Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD)","award":["XDA19030500"],"award-info":[{"award-number":["XDA19030500"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Understanding the long-term dynamics and driving factors behind small and micro wetlands is critical for their management and future sustainability. This study explored the impacts of natural and anthropogenic factors on the spatiotemporal evolution of these areas in Wuxi area using the support vector machine (SVM) classification method and the geographic detector model based on Landsat satellite image data from 1985 to 2020. The results revealed that: (1) Natural wetlands were prominent in Wuxi area, with an average proportion of 70%, and although they exhibited a downward trend over the last ten years, the scale of natural small and micro wetlands increased 1.5-fold\u2014from 4349.59 hm2 in 1985 to 10,841.59 hm2 in 2020. (2) The small and micro wetlands in Wuxi area had obvious seasonal variations, with most being 0.1\u20131 hm2 and 1\u20133 hm2, respectively. From the perspective of spatial distribution, they were primarily distributed in Yixing district, which accounts for 34% of Wuxi area. (3) The distribution of small and micro wetlands was systematically affected by natural and human activities. The main factors that affected the distribution of small and micro wetlands were the average annual temperature and GDP, with the interactions between all factors being nonlinear and bi-linear. The influences of natural factors on small and micro wetlands were weakened, with human activities steadily emerging as the dominant factor that affected their distribution. The results of this study can provide supportive data and a scientific basis for the ecological restoration and protection of wetlands.<\/jats:p>","DOI":"10.3390\/rs15041152","type":"journal-article","created":{"date-parts":[[2023,2,20]],"date-time":"2023-02-20T04:28:57Z","timestamp":1676867337000},"page":"1152","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":16,"title":["Evolution of Small and Micro Wetlands and Their Driving Factors in the Yangtze River Delta\u2014A Case Study of Wuxi Area"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1266-7665","authenticated-orcid":false,"given":"Jiamin","family":"Zhang","sequence":"first","affiliation":[{"name":"Joint Innovation Center for Modern Forestry Studies, College of Forestry, Nanjing Forestry University, Nanjing 210037, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lei","family":"Chu","sequence":"additional","affiliation":[{"name":"Joint Innovation Center for Modern Forestry Studies, College of Forestry, Nanjing Forestry University, Nanjing 210037, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1823-6049","authenticated-orcid":false,"given":"Zengxin","family":"Zhang","sequence":"additional","affiliation":[{"name":"Joint Innovation Center for Modern Forestry Studies, College of Forestry, Nanjing Forestry University, Nanjing 210037, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8045-3952","authenticated-orcid":false,"given":"Bin","family":"Zhu","sequence":"additional","affiliation":[{"name":"Joint Innovation Center for Modern Forestry Studies, College of Forestry, Nanjing Forestry University, Nanjing 210037, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaoyan","family":"Liu","sequence":"additional","affiliation":[{"name":"Joint Innovation Center for Modern Forestry Studies, College of Forestry, Nanjing Forestry University, Nanjing 210037, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qiang","family":"Yang","sequence":"additional","affiliation":[{"name":"College of Civil Engineering, Nanjing Forestry University, Nanjing 210037, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,2,20]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"3701","DOI":"10.1007\/s11053-020-09667-7","article-title":"Assessing and Modeling the Impacts of Wetland Land Cover Changes on Water Provision and Habitat Quality Ecosystem Services","volume":"29","author":"Rahimi","year":"2020","journal-title":"Nat. Resour. Res."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"109499","DOI":"10.1016\/j.jenvman.2019.109499","article-title":"Emergy and eco-exergy evaluation of wetland restoration based on the construction of a wetland landscape in the northwest Yunnan Plateau, China","volume":"252","author":"Sun","year":"2019","journal-title":"J. Environ. Manag."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Tornwall, B., Pitt, A., Brown, B., Hawley-Howard, J., and Baldwin, R. (2020). Diversity Patterns Associated with Varying Dispersal Capabilities as a Function of Spatial and Local Environmental Variables in Small Wetlands in Forested Ecosystems. Forests, 11.","DOI":"10.3390\/f11111146"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"203","DOI":"10.1111\/avsc.12144","article-title":"Small wetlands are critical for safeguarding rare and threatened plant species","volume":"18","author":"Richardson","year":"2015","journal-title":"Appl. Veg. Sci."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"041006","DOI":"10.1088\/2515-7620\/ac6859","article-title":"Role of small wetlands on the regime shift of ecological network in a wetlandscape","volume":"4","author":"Kim","year":"2022","journal-title":"Environ. Res. Commun."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"5038","DOI":"10.1002\/2016WR020102","article-title":"Biogeochemical hotspots: Role of small water bodies in landscape nutrient processing","volume":"53","author":"Cheng","year":"2017","journal-title":"Water Resour. Res."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"1510","DOI":"10.3390\/su5041510","article-title":"Impacts of Climatic Hazards on the Small Wetland Ecosystems (ponds): Evidence from Some Selected Areas of Coastal Bangladesh","volume":"5","author":"Rabbani","year":"2013","journal-title":"Sustainability"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"255","DOI":"10.1007\/s10750-015-2554-0","article-title":"Flood, drought and the inter-annual variation to the number and size of ponds and small wetlands in an English lowland landscape over three years of weather extremes","volume":"768","author":"Jeffries","year":"2016","journal-title":"Hydrobiologia"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"4005","DOI":"10.1007\/s10661-012-2845-0","article-title":"Dynamics of the lakes in the middle and lower reaches of the Yangtze River basin, China, since late nineteenth century","volume":"185","author":"Cui","year":"2013","journal-title":"Environ. Monit. Assess"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"108903","DOI":"10.1016\/j.ecolind.2022.108903","article-title":"Assessing degradation of lake wetlands in Bashang Plateau, China based on long-term time series Landsat images using wetland degradation index","volume":"139","author":"Zhu","year":"2022","journal-title":"Ecol. Indic."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"128080","DOI":"10.1016\/j.jhydrol.2022.128080","article-title":"Using cloud computing techniques to monitor long-term variations in ecohydrological dynamics of small seasonally-flooded wetlands in semi-arid South Africa","volume":"612","author":"Gxokwe","year":"2022","journal-title":"J. Hydrol."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"1265","DOI":"10.1016\/j.foreco.2011.01.005","article-title":"Characteristics of small headwater wetlands in second-growth forests of Washington, USA","volume":"261","author":"Janisch","year":"2011","journal-title":"Forest Ecol. Manag."},{"key":"ref_13","first-page":"173","article-title":"Mapping small wetlands of Kenya and Tanzania using remote sensing techniques","volume":"21","author":"Mwita","year":"2013","journal-title":"Int. J. Appl. Earth Obs."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"105695","DOI":"10.1016\/j.ecoleng.2019.105695","article-title":"The use of small-Unmanned Aerial Systems for high resolution analysis for intertidal wetland restoration schemes","volume":"143","author":"Dale","year":"2020","journal-title":"Ecol. Eng."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"13","DOI":"10.1016\/j.isprsjprs.2017.05.010","article-title":"Random forest wetland classification using ALOS-2 L-band, RADARSAT-2 C-band, and TerraSAR-X imagery","volume":"130","author":"Mahdianpari","year":"2017","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"312","DOI":"10.1016\/j.isprsjprs.2020.03.014","article-title":"Mapping coastal wetlands of China using time series Landsat images in 2018 and Google Earth Engine","volume":"163","author":"Wang","year":"2020","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_17","first-page":"353","article-title":"Remote sensing-based dynamic monitoring and environmental change of wetlands in southern Mongolian Plateau in 2000\u20132018","volume":"4","author":"Jie","year":"2021","journal-title":"China Geol."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Chu, L., Sun, T., Wang, T., Li, Z., and Cai, C. (2018). Evolution and Prediction of Landscape Pattern and Habitat Quality Based on CA-Markov and InVEST Model in Hubei Section of Three Gorges Reservoir Area (TGRA). Sustainability, 10.","DOI":"10.3390\/su10113854"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Guo, M., Li, J., Sheng, C., Xu, J., and Wu, L. (2017). A Review of Wetland Remote Sensing. Sensors, 17.","DOI":"10.3390\/s17040777"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"147644","DOI":"10.1016\/j.scitotenv.2021.147644","article-title":"Dynamic landscapes and the driving forces in the Yellow River Delta wetland region in the past four decades","volume":"787","author":"Zhang","year":"2021","journal-title":"Sci. Total Environ."},{"key":"ref_21","unstructured":"Li, Y. (2008). The Water Information Extraction and Land Cover Classification of Nansihu Lake Based on SPOT5 Remote Sensing Image. [Master\u2019s Thesis, Shandong University]."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"708","DOI":"10.1016\/j.scib.2018.05.002","article-title":"Long-term surface water changes and driving cause in Xiong\u2019an, China: From dense Landsat time series images and synthetic analysis","volume":"63","author":"Song","year":"2018","journal-title":"Sci. Bull."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Ogilvie, A., Poussin, J.-C., Bader, J.-C., Bayo, F., Bodian, A., Dacosta, H., Dia, D., Diop, L., Martin, D., and Sambou, S. (2020). Combining Multi-Sensor Satellite Imagery to Improve Long-Term Monitoring of Temporary Surface Water Bodies in the Senegal River Floodplain. Remote Sens., 12.","DOI":"10.3390\/rs12193157"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Cavallo, C., Papa, M.N., Gargiulo, M., Palau-Salvador, G., Vezza, P., and Ruello, G. (2021). Continuous Monitoring of the Flooding Dynamics in the Albufera Wetland (Spain) by Landsat-8 and Sentinel-2 Datasets. Remote Sens., 13.","DOI":"10.3390\/rs13173525"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"87","DOI":"10.1007\/s12652-021-03115-x","article-title":"Optimization effect of ecological restoration based on high-resolution remote sensing images in the ecological construction of soil and water conservation","volume":"13","author":"Fan","year":"2022","journal-title":"J. Amb. Intel. Hum. Comp."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Alibakhshi, S., Groen, T.A., Rautiainen, M., and Naimi, B. (2017). Remotely-Sensed Early Warning Signals of a Critical Transition in a Wetland Ecosystem. Remote Sens., 9.","DOI":"10.3390\/rs9040352"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Zhao, Y., Wang, S., Zhang, F., Shen, Q., Li, J., and Yang, F. (2021). Remote Sensing-Based Analysis of Spatial and Temporal Water Colour Variations in Baiyangdian Lake after the Establishment of the Xiong\u2019an New Area. Remote Sens., 13.","DOI":"10.3390\/rs13091729"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Doyle, C., Beach, T., and Luzzadder-Beach, S. (2021). Tropical Forest and Wetland Losses and the Role of Protected Areas in Northwestern Belize, Revealed from Landsat and Machine Learning. Remote Sens., 13.","DOI":"10.3390\/rs13030379"},{"key":"ref_29","first-page":"103202","article-title":"Evaluating capabilities of machine learning algorithms for aquatic vegetation classification in temperate wetlands using multi-temporal Sentinel-2 data","volume":"117","author":"Piaser","year":"2023","journal-title":"Int. J. Appl. Earth Obs."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"1161","DOI":"10.1071\/MF19062","article-title":"China\u2019s coastal-wetland change analysis based on high-resolution remote sensing","volume":"71","author":"Gao","year":"2020","journal-title":"Mar. Freshw. Res."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Banks, S., White, L., Behnamian, A., Chen, Z., Montpetit, B., Brisco, B., Pasher, J., and Duffe, J. (2019). Wetland Classification with Multi-Angle\/Temporal SAR Using Random Forests. Remote Sens., 11.","DOI":"10.3390\/rs11060670"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Wei, C., Guo, B., Fan, Y., Zang, W., and Ji, J. (2022). The Change Pattern and Its Dominant Driving Factors of Wetlands in the Yellow River Delta Based on Sentinel-2 Images. Remote Sens., 14.","DOI":"10.3390\/rs14174388"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Balogun, A.-L., Yekeen, S.T., Pradhan, B., and Althuwaynee, O.F. (2020). Spatio-Temporal Analysis of Oil Spill Impact and Recovery Pattern of Coastal Vegetation and Wetland Using Multispectral Satellite Landsat 8-OLI Imagery and Machine Learning Models. Remote Sens., 12.","DOI":"10.3390\/rs12071225"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"123475","DOI":"10.1016\/j.jclepro.2020.123475","article-title":"Wetland conversion risk assessment of East Kolkata Wetland: A Ramsar site using random forest and support vector machine model","volume":"275","author":"Ghosh","year":"2020","journal-title":"J. Clean. Prod."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"107764","DOI":"10.1016\/j.ecolind.2021.107764","article-title":"Identification and scenario prediction of degree of wetland damage in Guangxi based on the CA-Markov model","volume":"127","author":"Zhang","year":"2021","journal-title":"Ecol. Indic."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"2644","DOI":"10.1002\/ldr.2939","article-title":"China\u2019s wetlands loss to urban expansion","volume":"29","author":"Mao","year":"2018","journal-title":"Land Degrad. Dev."},{"key":"ref_37","first-page":"102874","article-title":"Centennial-scale study on the spatial-temporal evolution of riparian wetlands in the Yangtze River of China","volume":"113","author":"Chen","year":"2022","journal-title":"Int. J. Appl. Earth Obs."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"593","DOI":"10.1080\/01426397.2012.672640","article-title":"Historical Changes in the Distribution and Abundance of Constructed Ponds in Response to Changing Population Density and Land Use","volume":"38","author":"Fairchild","year":"2013","journal-title":"Landsc. Res."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"827","DOI":"10.1007\/s10980-019-00806-x","article-title":"Effects of cropland encroachment on prairie pothole wetlands: Numbers, density, size, shape, and structural connectivity","volume":"34","author":"Johnston","year":"2019","journal-title":"Landsc. Ecol."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"e2482","DOI":"10.1016\/j.heliyon.2019.e02482","article-title":"A quantitative assessment of the contribution of small standing water bodies to the European waterscapes\u2014Case of Estonia and France","volume":"5","author":"Terasmaa","year":"2019","journal-title":"Heliyon"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"125038","DOI":"10.1016\/j.jhydrol.2020.125038","article-title":"An investigation of the hydrological influence on the distribution and transition of wetland cover in a complex lake\u2013floodplain system using time-series remote sensing and hydrodynamic simulation","volume":"587","author":"Liang","year":"2020","journal-title":"J. Hydrol."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"146481","DOI":"10.1016\/j.scitotenv.2021.146481","article-title":"Influences of pesticides, nutrients, and local environmental variables on phytoplankton communities in lentic small water bodies in a German lowland agricultural area","volume":"780","author":"Wijewardene","year":"2021","journal-title":"Sci. Total Environ."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"108069","DOI":"10.1016\/j.ecolind.2021.108069","article-title":"Spatial and temporal evolution characteristics of the water conservation function and its driving factors in regional lake wetlands\u2014Two types of homogeneous lakes as examples","volume":"130","author":"Hu","year":"2021","journal-title":"Ecol. Indic."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"250","DOI":"10.1016\/j.ecolind.2016.02.052","article-title":"A measure of spatial stratified heterogeneity","volume":"67","author":"Wang","year":"2016","journal-title":"Ecol. Indic."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"147937","DOI":"10.1016\/j.scitotenv.2021.147937","article-title":"Interactive effects of natural and anthropogenic factors on heterogenetic accumulations of heavy metals in surface soils through geodetector analysis","volume":"789","author":"Huang","year":"2021","journal-title":"Sci. Total Environ."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"108655","DOI":"10.1016\/j.ecolind.2022.108655","article-title":"Analysis of the heterogeneity of urban expansion landscape patterns and driving factors based on a combined Multi-Order Adjacency Index and Geodetector model","volume":"136","author":"Liu","year":"2022","journal-title":"Ecol. Indic."},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Wang, S., Zhang, L., Zhang, H., Han, X., and Zhang, L. (2020). Spatial\u2014Temporal Wetland Landcover Changes of Poyang Lake Derived from Landsat and HJ-1A\/B Data in the Dry Season from 1973\u20132019. Remote Sens., 12.","DOI":"10.3390\/rs12101595"},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Wang, Y., Zhang, Z., and Chen, X. (2021). Quantifying Influences of Natural and Anthropogenic Factors on Vegetation Changes Based on Geodetector: A Case Study in the Poyang Lake Basin, China. Remote Sens., 13.","DOI":"10.3390\/rs13245081"},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Wang, Y., Wen, H., Sun, D., and Li, Y. (2021). Quantitative Assessment of Landslide Risk Based on Susceptibility Mapping Using Random Forest and GeoDetector. Remote Sens., 13.","DOI":"10.3390\/rs13132625"},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"108619","DOI":"10.1016\/j.ecolind.2022.108619","article-title":"Evaluation and analysis of ecosystem service value based on land use\/cover change in Dongting Lake wetland","volume":"136","author":"Long","year":"2022","journal-title":"Ecol. Indic."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"131105","DOI":"10.1016\/j.jclepro.2022.131105","article-title":"Dynamic identification and health assessment of wetlands in the middle reaches of the Yangtze River basin under changing environment","volume":"345","author":"Liu","year":"2022","journal-title":"J. Clean. Prod."},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Xing, L., Niu, Z., Jiao, C., Zhang, J., Han, S., Cheng, G., and Wu, J. (2022). A Novel Workflow for Seasonal Wetland Identification Using Bi-Weekly Multiple Remote Sensing Data. Remote Sens., 14.","DOI":"10.3390\/rs14041037"},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Xiaoli, H., Xin, L., and Ling, L. (2018). Modeling the Land Use Change in an Arid Oasis Constrained by Water Resources and Environmental Policy Change Using Cellular Automata Models. Sustainability, 10.","DOI":"10.3390\/su10082878"},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Koko, A., Yue, W., Abubakar, G., Hamed, R., and Noman, A.A. (2020). Monitoring and Predicting Spatio-Temporal Land Use\/Land Cover Changes in Zaria City, Nigeria, through an Integrated Cellular Automata and Markov Chain Model (CA-Markov). Sustainability, 12.","DOI":"10.3390\/su122410452"},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"Siddique, M.A., Wang, Y., Xu, N., Ullah, N., and Zeng, P. (2021). The Spatiotemporal Implications of Urbanization for Urban Heat Islands in Beijing: A Predictive Approach Based on CA\u2014Markov Modeling (2004\u20132050). Remote Sens., 13.","DOI":"10.3390\/rs13224697"},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"2188","DOI":"10.1080\/13658816.2016.1165228","article-title":"Driving forces and their interactions of built-up land expansion based on the geographical detector\u2014A case study of Beijing, China","volume":"30","author":"Ju","year":"2016","journal-title":"Int. J. Geogr. Inf. Sci. IJGIS"},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"271","DOI":"10.1007\/s11442-019-1596-z","article-title":"Quantitative attribution analysis of soil erosion in different geomorphological types in karst areas: Based on the geodetector method","volume":"29","author":"Huan","year":"2019","journal-title":"J. Geogr. Sci."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"593","DOI":"10.1080\/15481603.2020.1760434","article-title":"An optimal parameters-based geographical detector model enhances geographic characteristics of explanatory variables for spatial heterogeneity analysis: Cases with different types of spatial data","volume":"57","author":"Song","year":"2020","journal-title":"Gisci. Remote Sens."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"109568","DOI":"10.1016\/j.ecolind.2022.109568","article-title":"Analysis of the heterogeneity of landscape risk evolution and driving factors based on a combined GeoDa and Geodetector model","volume":"144","author":"Ren","year":"2022","journal-title":"Ecol. Indic."},{"key":"ref_60","doi-asserted-by":"crossref","unstructured":"Ji, B., Qin, Y., Zhang, T., Zhou, X., Yi, G., Zhang, M., and Li, M. (2022). Analyzing Driving Factors of Drought in Growing Season in the Inner Mongolia Based on Geodetector and GWR Models. Remote Sens., 14.","DOI":"10.3390\/rs14236007"},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"106545","DOI":"10.1016\/j.ecolind.2020.106545","article-title":"Applying Geodetector to disentangle the contributions of natural and anthropogenic factors to NDVI variations in the middle reaches of the Heihe River Basin","volume":"117","author":"Zhu","year":"2020","journal-title":"Ecol. Indic."},{"key":"ref_62","doi-asserted-by":"crossref","unstructured":"Wang, K., Cao, C., Xie, B., Xu, M., Yang, X., Guo, H., and Duerler, R.S. (2022). Analysis of the Spatial and Temporal Evolution Patterns of Grassland Health and Its Driving Factors in Xilingol. Remote Sens., 14.","DOI":"10.3390\/rs14205179"},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"109287","DOI":"10.1016\/j.ecolind.2022.109287","article-title":"Spatiotemporal variability of extreme precipitation at different time scales and quantitative analysis of associated driving teleconnection factors: Insights from Taihu Basin, China","volume":"142","author":"Wang","year":"2022","journal-title":"Ecol. Indic."},{"key":"ref_64","doi-asserted-by":"crossref","unstructured":"Gxokwe, S., Dube, T., and Mazvimavi, D. (2020). Multispectral Remote Sensing of Wetlands in Semi-Arid and Arid Areas: A Review on Applications, Challenges and Possible Future Research Directions. Remote Sens., 12.","DOI":"10.3390\/rs12244190"},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"106175","DOI":"10.1016\/j.ocecoaman.2022.106175","article-title":"Annual variation of the landscape pattern in the Liao River Delta wetland from 1976 to 2020","volume":"224","author":"Chen","year":"2022","journal-title":"Ocean. Coast. Manag."},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"1102","DOI":"10.1080\/15481603.2020.1846948","article-title":"A large-scale change monitoring of wetlands using time series Landsat imagery on Google Earth Engine: A case study in Newfoundland","volume":"57","author":"Mahdianpari","year":"2020","journal-title":"Gisci. Remote Sens."},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"799","DOI":"10.1016\/j.rse.2017.09.023","article-title":"Wetland changes of China\u2019s largest freshwater lake and their linkage with the Three Gorges Dam","volume":"204","author":"Han","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_68","first-page":"100569","article-title":"A simple and robust wetland classification approach by using optical indices, unsupervised and supervised machine learning algorithms","volume":"23","author":"Ahmed","year":"2021","journal-title":"Remote Sens. Appl. Soc. Environ."},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"294","DOI":"10.1016\/j.ecolind.2018.12.032","article-title":"Improvements to the Wetland Extent Trends (WET) index as a tool for monitoring natural and human-made wetlands","volume":"99","author":"Darrah","year":"2019","journal-title":"Ecol. Indic."},{"key":"ref_70","doi-asserted-by":"crossref","unstructured":"Wen, L., and Hughes, M.G. (2022). Coastal Wetland Responses to Sea Level Rise: The Losers and Winners Based on Hydro-Geomorphological Settings. Remote Sens., 14.","DOI":"10.3390\/rs14081888"},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"109689","DOI":"10.1016\/j.ecolind.2022.109689","article-title":"Analysis of long-term wetland variations in China using land use\/land cover dataset derived from Landsat images","volume":"145","author":"An","year":"2022","journal-title":"Ecol. Indic."},{"key":"ref_72","first-page":"102383","article-title":"Tracking changes in aquaculture ponds on the China coast using 30 years of Landsat images","volume":"102","author":"Duan","year":"2021","journal-title":"Int. J. Appl. Earth Obs."},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"152339","DOI":"10.1016\/j.scitotenv.2021.152339","article-title":"Increasing fragmentation and squeezing of coastal wetlands: Status, drivers, and sustainable protection from the perspective of remote sensing","volume":"811","author":"Wu","year":"2022","journal-title":"Sci. Total Environ."},{"key":"ref_74","doi-asserted-by":"crossref","first-page":"435","DOI":"10.1007\/s12665-014-3565-2","article-title":"Impacts of human activities on the evolution of estuarine wetland in the Yangtze Delta from 2000 to 2010","volume":"73","author":"Zhang","year":"2015","journal-title":"Environ. Earth Sci."},{"key":"ref_75","doi-asserted-by":"crossref","unstructured":"Yang, H., Wang, H., Lu, J., Zhou, Z., Feng, Q., and Wu, Y. (2021). Full Lifecycle Monitoring on Drought-Converted Catastrophic Flood Using Sentinel-1 SAR: A Case Study of Poyang Lake Region during Summer 2020. Remote Sens., 13.","DOI":"10.3390\/rs13173485"},{"key":"ref_76","doi-asserted-by":"crossref","unstructured":"Chen, H., Zhang, W., Gao, H., and Nie, N. (2018). Climate Change and Anthropogenic Impacts on Wetland and Agriculture in the Songnen and Sanjiang Plain, Northeast China. Remote Sens., 10.","DOI":"10.3390\/rs10030356"},{"key":"ref_77","doi-asserted-by":"crossref","first-page":"151","DOI":"10.1007\/s00027-012-0278-z","article-title":"Current state of knowledge regarding the world\u2019s wetlands and their future under global climate change: A synthesis","volume":"75","author":"Junk","year":"2013","journal-title":"Aquat. Sci."},{"key":"ref_78","doi-asserted-by":"crossref","unstructured":"You, H., Fan, H., Xu, L., Wu, Y., Liu, L., and Yao, Z. (2019). Poyang Lake Wetland Ecosystem Health Assessment of Using the Wetland Landscape Classification Characteristics. Water, 11.","DOI":"10.3390\/w11040825"},{"key":"ref_79","doi-asserted-by":"crossref","first-page":"138096","DOI":"10.1016\/j.scitotenv.2020.138096","article-title":"Monitoring the spatio-temporal dynamics of the wetland vegetation in Poyang Lake by Landsat and MODIS observations","volume":"725","author":"Mu","year":"2020","journal-title":"Sci. Total Environ."},{"key":"ref_80","doi-asserted-by":"crossref","first-page":"113","DOI":"10.1016\/j.scitotenv.2014.09.078","article-title":"Increased tree establishment in Lithuanian peat bogs\u2014Insights from field and remotely sensed approaches","volume":"505","author":"Edvardsson","year":"2015","journal-title":"Sci. Total Environ."},{"key":"ref_81","doi-asserted-by":"crossref","first-page":"151310","DOI":"10.1016\/j.scitotenv.2021.151310","article-title":"Spatiotemporal dynamics of wetlands and their driving factors based on PLS-SEM: A case study in Wuhan","volume":"806","author":"Wang","year":"2021","journal-title":"Sci. Total Environ."},{"key":"ref_82","doi-asserted-by":"crossref","first-page":"133","DOI":"10.1007\/s00027-012-0262-7","article-title":"Landscape and climate change threats to wetlands of North and Central America","volume":"75","author":"William","year":"2013","journal-title":"Aquat. Sci."},{"key":"ref_83","doi-asserted-by":"crossref","first-page":"144038","DOI":"10.1016\/j.scitotenv.2020.144038","article-title":"Functions of constructed wetland animals in water environment protection\u2014A critical review","volume":"760","author":"Li","year":"2021","journal-title":"Sci. Total Environ."},{"key":"ref_84","doi-asserted-by":"crossref","first-page":"106848","DOI":"10.1016\/j.ecoleng.2022.106848","article-title":"Long-term variation characteristics of nutrients in the water and sediments of a surface flow constructed wetland with micro-polluted water sources","volume":"187","author":"Zhu","year":"2023","journal-title":"Ecol. Eng."},{"key":"ref_85","first-page":"191","article-title":"Urban expansion induced vulnerability assessment of East Kolkata Wetland using Fuzzy MCDM method","volume":"13","author":"Ghosh","year":"2019","journal-title":"Remote Sens. Appl. Soc. Environ."},{"key":"ref_86","doi-asserted-by":"crossref","unstructured":"Ding, Q., Chen, Y., Bu, L., and Ye, Y. (2021). Multi-Scenario Analysis of Habitat Quality in the Yellow River Delta by Coupling FLUS with InVEST Model. Int. J. Environ. Res. Public Health, 18.","DOI":"10.3390\/ijerph18052389"},{"key":"ref_87","doi-asserted-by":"crossref","first-page":"109307","DOI":"10.1016\/j.ecolind.2022.109307","article-title":"Global occupation of wetland by artificial impervious surface area expansion and its impact on ecosystem service value for 2001\u20132018","volume":"142","author":"Yang","year":"2022","journal-title":"Ecol. Indic."},{"key":"ref_88","doi-asserted-by":"crossref","first-page":"158","DOI":"10.1016\/j.gloenvcha.2017.04.001","article-title":"Future impacts of drivers of change on wetland ecosystem services in Colombia","volume":"44","author":"Ricaurte","year":"2017","journal-title":"Glob. Environ. Chang."},{"key":"ref_89","doi-asserted-by":"crossref","unstructured":"Dang, A.T.N., Kumar, L., Reid, M., and Nguyen, H. (2021). Remote Sensing Approach for Monitoring Coastal Wetland in the Mekong Delta, Vietnam: Change Trends and Their Driving Forces. Remote Sens., 13.","DOI":"10.3390\/rs13173359"},{"key":"ref_90","first-page":"320","article-title":"Studies on the Changes of Landscape Patterns of Hangzhou Xixi Wetland in 15 years","volume":"23","author":"Yu","year":"2007","journal-title":"Bull. Sci. Technol."},{"key":"ref_91","doi-asserted-by":"crossref","first-page":"128346","DOI":"10.1016\/j.jclepro.2021.128346","article-title":"Restoring natural wetlands through financial incentives based adoption of constructed wetlands on agricultural farms","volume":"317","author":"Ranjan","year":"2021","journal-title":"J. Clean. Prod."},{"key":"ref_92","doi-asserted-by":"crossref","unstructured":"Xu, Y., Zhang, X., Liu, X., and Zhang, Z. (2022). Biodiversity and Spatiotemporal Distribution of Spontaneous Vegetation in Tangdao Bay National Wetland Park, Qingdao City, China. Int. J. Environ. Res. Public Health, 19.","DOI":"10.3390\/ijerph191811665"},{"key":"ref_93","doi-asserted-by":"crossref","first-page":"61","DOI":"10.1021\/es101403q","article-title":"Constructed Wetlands for Wastewater Treatment: Five Decades of Experience","volume":"45","author":"Vymazal","year":"2011","journal-title":"Environ. Sci. Technol."},{"key":"ref_94","doi-asserted-by":"crossref","unstructured":"Gabr, M.E., Al-Ansari, N., Salem, A., and Awad, A. (2023). Proposing a Wetland-Based Economic Approach for Wastewater Treatment in Arid Regions as an Alternative Irrigation Water Source. Hydrology, 10.","DOI":"10.3390\/hydrology10010020"},{"key":"ref_95","doi-asserted-by":"crossref","first-page":"386","DOI":"10.1016\/j.rse.2018.11.038","article-title":"Regional differences of lake evolution across China during 1960s\u20132015 and its natural and anthropogenic causes","volume":"221","author":"Zhang","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_96","doi-asserted-by":"crossref","first-page":"109295","DOI":"10.1016\/j.ecolind.2022.109295","article-title":"Spatiotemporal dynamics of lake wetland in the Wanjiang Plain of the Yangtze River basin, China during the recent century","volume":"142","author":"Dong","year":"2022","journal-title":"Ecol. Indic."},{"key":"ref_97","doi-asserted-by":"crossref","first-page":"117110","DOI":"10.1016\/j.jenvman.2022.117110","article-title":"Combining historical maps and landsat images to delineate the centennial-scale changes of lake wetlands in Taihu Lake Basin, China","volume":"329","author":"Yang","year":"2023","journal-title":"J. Environ. Manag."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/4\/1152\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T18:37:06Z","timestamp":1760121426000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/4\/1152"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,2,20]]},"references-count":97,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2023,2]]}},"alternative-id":["rs15041152"],"URL":"https:\/\/doi.org\/10.3390\/rs15041152","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,2,20]]}}}