{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,26]],"date-time":"2025-11-26T16:43:08Z","timestamp":1764175388632,"version":"build-2065373602"},"reference-count":43,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2023,3,1]],"date-time":"2023-03-01T00:00:00Z","timestamp":1677628800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Natural Science Foundation of China","award":["42076184","41876109","41706195","2017YFC1404902","2016YFC1401007","41-Y30B12-9001-14\/16"],"award-info":[{"award-number":["42076184","41876109","41706195","2017YFC1404902","2016YFC1401007","41-Y30B12-9001-14\/16"]}]},{"name":"National Key Research and Development Program of China","award":["42076184","41876109","41706195","2017YFC1404902","2016YFC1401007","41-Y30B12-9001-14\/16"],"award-info":[{"award-number":["42076184","41876109","41706195","2017YFC1404902","2016YFC1401007","41-Y30B12-9001-14\/16"]}]},{"name":"National High Resolution Special Research","award":["42076184","41876109","41706195","2017YFC1404902","2016YFC1401007","41-Y30B12-9001-14\/16"],"award-info":[{"award-number":["42076184","41876109","41706195","2017YFC1404902","2016YFC1401007","41-Y30B12-9001-14\/16"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Mangroves are an important source of blue carbon that grow in coastal areas. The study of mangrove species distribution is the basis of carbon storage research. In this study, we explored the potential of combining optical (Gaofen-1, Sentinel-2, and Landsat-9) and fully polarized synthetic aperture radar data from different periods (Gaofen-3) to distinguish mangrove species in the Fucheng town of Leizhou, Guangdong Province. The Gaofen-1 data were fused with Sentinel-2 and Landsat-9 satellite data, respectively. The new data after fusion had both high spatial and spectral resolution. The backscattering coefficient and polarization decomposition parameters of the fully polarized SAR data which could characterize the canopy structure of mangroves were extracted. Ten different feature combinations were designed by combining the two types of data. The extremely randomized trees algorithm (ERT) was used to classify the species, and the optimal feature subset was selected by the feature selection algorithm on the basis of the ERT, and the importance of the features was sorted. Studies show the following: (1) When controlling a single variable, the higher the spatial resolution of the multi-spectral data, the higher the interspecific classification accuracy. (2) The coupled Sentinel-2 and Landsat-9 data with a 2 m resolution will have higher classification accuracy than a single data source. (3) The selected feature subset contains all types of features in the optical data and the polarization decomposition features of the SAR data from different periods: multi-spectral band &gt; texture feature &gt; polarization decomposition parameter &gt; vegetation index. Among the optimized feature combinations, the classification accuracy of mangrove species was the highest, the overall classification accuracy was 90.13%, and Kappa was 0.84, indicating that multi-source and SAR data from different periods coupling could improve the discrimination of mangrove species. (4) The ERT classification algorithm is suitable for the study of mangrove species classification, and the classification accuracy of extremely random trees in this paper is higher than that of random forest (RF), K-nearest neighbor (KNN), and Bayesian (Bayes). The results can provide technical guidance and data support for mangrove species monitoring based on multi-source satellite data.<\/jats:p>","DOI":"10.3390\/rs15051386","type":"journal-article","created":{"date-parts":[[2023,3,1]],"date-time":"2023-03-01T03:28:06Z","timestamp":1677641286000},"page":"1386","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":25,"title":["Performance Evaluation of Mangrove Species Classification Based on Multi-Source Remote Sensing Data Using Extremely Randomized Trees in Fucheng Town, Leizhou City, Guangdong Province"],"prefix":"10.3390","volume":"15","author":[{"given":"Xinzhe","family":"Wang","sequence":"first","affiliation":[{"name":"Institute of Information Science and Engineering, Dalian Polytechnic University, Dalian 116034, China"}]},{"given":"Linlin","family":"Tan","sequence":"additional","affiliation":[{"name":"Institute of Information Science and Engineering, Dalian Polytechnic University, Dalian 116034, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3121-9289","authenticated-orcid":false,"given":"Jianchao","family":"Fan","sequence":"additional","affiliation":[{"name":"Department of Marine Remote Sensing, National Marine Environmental Monitoring Center, Dalian 116023, China"}]}],"member":"1968","published-online":{"date-parts":[[2023,3,1]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Cao, J., Liu, K., Liu, L., Zhu, Y., Li, J., and He, Z. (2018). Identifying mangrove species using field close-range snapshot hyperspectral imaging and machine-learning techniques. Remote Sens., 10.","DOI":"10.3390\/rs10122047"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"89","DOI":"10.1146\/annurev-environ-101718-033302","article-title":"The state of the world\u2019s mangrove forests: Past, present, and future","volume":"44","author":"Friess","year":"2019","journal-title":"Annu. Rev. Environ. Resour."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"217","DOI":"10.1016\/S0378-1127(97)00289-2","article-title":"Mangrove vegetation assessment in the santiago river mouth, mexico, by means of supervised classification using landsat tm imagery","volume":"105","year":"1998","journal-title":"For. Ecol. Manag."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Ghosh, M.K., Kumar, L., and Roy, C. (2016). Mapping long-term changes in mangrove species composition and distribution in the sundarbans. Forests, 7.","DOI":"10.3390\/f7120305"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"188","DOI":"10.2747\/1548-1603.45.2.188","article-title":"Identifying mangrove species and their surrounding land use and land cover classes using an object-oriented approach with a lacunarity spatial measure","volume":"45","author":"Myint","year":"2008","journal-title":"GIScience Remote Sens."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"359","DOI":"10.1007\/s11852-014-0322-3","article-title":"A study on abundance and distribution of mangrove species in indian sundarban using remote sensing technique","volume":"18","author":"Giri","year":"2014","journal-title":"J. Coast. Conserv."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"393","DOI":"10.1109\/LGRS.2009.2014398","article-title":"Evaluation of morphological texture features for mangrove forest mapping and species discrimination using multispectral ikonos imagery","volume":"6","author":"Huang","year":"2009","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Wang, T., Zhang, H., Lin, H., and Fang, C. (2016). Textural\u2013spectral feature-based species classification of mangroves in mai po nature reserve from worldview-3 imagery. Remote Sens., 8.","DOI":"10.3390\/rs8010024"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"12192","DOI":"10.3390\/rs70912192","article-title":"Retrieval of mangrove aboveground biomass at the individual species level with worldview-2 images","volume":"7","author":"Zhu","year":"2015","journal-title":"Remote Sens."},{"key":"ref_10","first-page":"34","article-title":"Estimation of aboveground biomass in mangrove forests using high-resolution satellite data","volume":"19","author":"Hirata","year":"2014","journal-title":"J. For.-JPN"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"861","DOI":"10.1007\/s13157-017-0925-1","article-title":"Mangrove species discrimination from very high resolution imagery using gaussian markov random field model","volume":"38","author":"Wan","year":"2018","journal-title":"Wetlands"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Wang, D., Wan, B., Qiu, P., Su, Y., Guo, Q., and Wu, X. (2018). Artificial mangrove species mapping using pl\u00e9iades-1: An evaluation of pixel-based and object-based classifications with selected machine learning algorithms. Remote Sens., 10.","DOI":"10.3390\/rs10020294"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Wang, D., Wan, B., Qiu, P., Su, Y., Guo, Q., Wang, R., Sun, F., and Wu, X. (2018). Evaluating the performance of sentinel-2, landsat 8 and pl\u00e9iades-1 in mapping mangrove extent and species. Remote Sens., 10.","DOI":"10.3390\/rs10091468"},{"key":"ref_14","first-page":"273","article-title":"Mangrove classification using airborne hyperspectral aviris-ng and comparing with other spaceborne hyperspectral and multispectral data","volume":"24","author":"Hati","year":"2021","journal-title":"Egypt. J. Remote Sens. Space Sci."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Zhu, Y., Liu, K., Myint, S.W., Du, Z., Li, Y., Cao, J., Liu, L., and Wu, Z. (2020). Integration of gf2 optical, gf3 sar, and uav data for estimating aboveground biomass of chia\u2019s largest artificially planted mangroves. Remote Sens., 12.","DOI":"10.3390\/rs12122039"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Zhang, H., Wang, T., Liu, M., Jia, M., Lin, H., Chu, L.M., and Devlin, A.T. (2018). Potential of combining optical and dual polarimetric sar data for improving mangrove species discrimination using rotation forest. Remote Sens., 10.","DOI":"10.3390\/rs10030467"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"198","DOI":"10.1080\/14888386.2020.1843540","article-title":"Biodiversity assessment of indian mangroves using in situ observations and remotely sensed data","volume":"21","author":"Kripa","year":"2020","journal-title":"Biodiversity"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Ma, C., Ai, B., Zhao, J., Xu, X., and Huang, W. (2019). Change detection of mangrove forests in coastal guangdong during the past three decades based on remote sensing data. Remote Sens., 11.","DOI":"10.3390\/rs11080921"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"7828","DOI":"10.1080\/01431161.2014.978034","article-title":"Combining eo-1 hyperion and envisat asar data for mangrove species classification in mai po ramsar site, hong kong","volume":"35","author":"Wong","year":"2014","journal-title":"Int. J. Remote Sens."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"174","DOI":"10.1007\/s11676-009-0032-0","article-title":"The mangrove and its conservation in leizhou peninsula, china","volume":"20","author":"Gao","year":"2009","journal-title":"J. For. Res."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"401","DOI":"10.1007\/s11284-007-0393-9","article-title":"Restoration of mangrove plantations and colonisation by native species in leizhou bay, south china","volume":"23","author":"Ren","year":"2008","journal-title":"Ecol. Res."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"412","DOI":"10.1109\/36.134090","article-title":"Dependence of radar backscatter on coniferous forest biomass","volume":"30","author":"Dobson","year":"1992","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"245","DOI":"10.1002\/aqc.833","article-title":"The potential of l-band sar for quantifying mangrove characteristics and change: Case studies from the tropics","volume":"17","author":"Mitchell","year":"2007","journal-title":"Aquat. Conserv. Mar. Freshw. Ecosyst."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Brown, I., Mwansasu, S., and Westerberg, L.O. (2016). L-band polarimetric target decomposition of mangroves of the rufiji delta, tanzania. Remote Sens., 8.","DOI":"10.3390\/rs8020140"},{"key":"ref_25","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_26","doi-asserted-by":"crossref","first-page":"119","DOI":"10.1016\/0034-4257(94)90134-1","article-title":"modified soil adjusted vegetation index","volume":"48","author":"Qi","year":"1994","journal-title":"Remote Sens. Environ."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"229","DOI":"10.1016\/S0034-4257(00)00113-9","article-title":"Estimating corn leaf chlorophyll concentration from leaf and canopy reflectance","volume":"74","author":"Daughtry","year":"2000","journal-title":"Remote Sens. Environ."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"83","DOI":"10.1016\/j.isprsjprs.2013.04.007","article-title":"Evaluating the capabilities of sentinel-2 for quantitative estimation of biophysical variables in vegetation","volume":"82","author":"Frampton","year":"2013","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_29","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_30","doi-asserted-by":"crossref","first-page":"273","DOI":"10.1016\/S0034-4257(98)00059-5","article-title":"Quantifying chlorophylls and caroteniods at leaf and canopy scales: An evaluation of some hyperspectral approaches","volume":"66","author":"Blackburn","year":"1998","journal-title":"Remote Sens. Environ."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"7063","DOI":"10.3390\/s110707063","article-title":"Evaluation of sentinel-2 red-edge bands for empirical estimation of green lai and chlorophyll content","volume":"11","author":"Delegido","year":"2011","journal-title":"Sensors"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"610","DOI":"10.1109\/TSMC.1973.4309314","article-title":"Textural features for image classification","volume":"6","author":"Haralick","year":"1973","journal-title":"IEEE Trans. Syst. Man Cybern."},{"key":"ref_33","first-page":"1","article-title":"Mangrove species classification in Hainan bamen Bay based on GF optics and fully polarimetric SAR","volume":"41","author":"Zhang","year":"2022","journal-title":"J. Trop. Oceanogr."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"95","DOI":"10.1016\/j.isprsjprs.2020.06.001","article-title":"Development and application of a new mangrove vegetation index (mvi) for rapid and accurate mangrove mapping","volume":"166","author":"Baloloy","year":"2020","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Shang, K., Yao, Y., Li, Y., Yang, J., and Guo, X. (2020). Fusion of five satellite-derived products using extremely randomized trees to estimate terrestrial latent heat flux over europe. Remote Sens., 12.","DOI":"10.3390\/rs12040687"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"7563","DOI":"10.1007\/s00521-019-04287-6","article-title":"A data ensemble approach for real-time air quality forecasting using extremely randomized trees and deep neural networks","volume":"32","author":"Eslami","year":"2020","journal-title":"Neural Comput. Appl."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"125130","DOI":"10.1016\/j.jhydrol.2020.125130","article-title":"Modelling of daily lake surface water temperature from air temperature: Extremely randomized trees (ert) versus air2water, mars, m5tree, rf and mlpnn","volume":"588","author":"Heddam","year":"2020","journal-title":"J. Hydrol."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Bunting, P., Rosenqvist, A., Lucas, R.M., Rebelo, L.M., Hilarides, L., Thomas, N., Hardy, A., Itoh, T., Shimada, M., and Finlayson, C.M. (2018). The global mangrove watch\u2014A new 2010 global baseline of mangrove extent. Remote Sens., 10.","DOI":"10.3390\/rs10101669"},{"key":"ref_39","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_40","doi-asserted-by":"crossref","first-page":"3712","DOI":"10.1109\/JSTARS.2015.2454297","article-title":"Efficiency assessment of multitemporal c-band radarsat-2 intensity and landsat-8 surface reflectance satellite imagery for crop classification in ukraine","volume":"9","author":"Skakun","year":"2015","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_41","first-page":"99","article-title":"Effects of spatial resolution and texture features on multi-spectral remote sensing classification","volume":"20","author":"Yang","year":"2018","journal-title":"J. Geo-Inf. Sci."},{"key":"ref_42","first-page":"246","article-title":"Response of spatial scale for land cover classification of remote sensing","volume":"20","author":"Xu","year":"2018","journal-title":"Geo-Inf. Sci."},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Xia, J., Yokoya, N., and Pham, T.D. (2020). Probabilistic mangrove species mapping with multiple-source remote-sensing datasets using label distribution learning in xuan thuy national park, vietnam. Remote Sens., 12.","DOI":"10.3390\/rs12223834"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/5\/1386\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T18:45:03Z","timestamp":1760121903000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/5\/1386"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,3,1]]},"references-count":43,"journal-issue":{"issue":"5","published-online":{"date-parts":[[2023,3]]}},"alternative-id":["rs15051386"],"URL":"https:\/\/doi.org\/10.3390\/rs15051386","relation":{},"ISSN":["2072-4292"],"issn-type":[{"type":"electronic","value":"2072-4292"}],"subject":[],"published":{"date-parts":[[2023,3,1]]}}}