Tracking annual dynamics of mangrove forests in mangrove National Nature Reserves of China based on time series Sentinel-2 imagery during 2016–2020

Baloloy, 2020, Development and application of a new mangrove vegetation index (MVI) for rapid and accurate mangrove mapping, ISPRS J. Photogramm. Remote Sens., 166, 95, 10.1016/j.isprsjprs.2020.06.001 Belgiu, 2018, Sentinel-2 cropland mapping using pixel-based and object-based time-weighted dynamic time warping analysis, Remote Sens. Environ., 204, 509, 10.1016/j.rse.2017.10.005 Belgiu, 2016, Random forest in remote sensing: A review of applications and future directions, ISPRS J. Photogramm. Remote Sens., 114, 24, 10.1016/j.isprsjprs.2016.01.011 Breiman, 2001, Random forests, Mach. Learn., 45, 5, 10.1023/A:1010933404324 Cao, 2018, Object-Based Mangrove Species Classification Using Unmanned Aerial Vehicle Hyperspectral Images and Digital Surface Models, Remote Sens., 10, 89, 10.3390/rs10010089 Costa, 2017, Using mixed objects in the training of object-based image classifications, Remote Sens. Environ., 190, 188, 10.1016/j.rse.2016.12.017 Du, 2015, Random Forest and Rotation Forest for fully polarized SAR image classification using polarimetric and spatial features, ISPRS J. Photogramm. Remote Sens., 105, 38, 10.1016/j.isprsjprs.2015.03.002 Fan, 2018, The history, achievements and lessons learnt for mangrove restoration in Guangxi, China, Guangxi Sci, 25, 363 Gao, 2019, Spartina alterniflora invasion alters soil bacterial communities and enhances soil N2O emissions by stimulating soil denitrification in mangrove wetland, Sci. Total Environ., 653, 231, 10.1016/j.scitotenv.2018.10.277 Goldberg, 2020, Global declines in human-driven mangrove loss, Glob. Change Biol., 26, 5844, 10.1111/gcb.15275 Guo, 2021, Estimating mangrove leaf area index based on red-edge vegetation indices: A comparison among UAV, WorldView-2 and Sentinel-2 imagery, Int. J. Appl. Earth Obs. Geoinf., 103 Holloway, 2018, Statistical machine learning methods and remote sensing for sustainable development goals: a review, Remote Sens., 10, 1365, 10.3390/rs10091365 Hossain, 2019, Segmentation for Object-Based Image Analysis (OBIA): A review of algorithms and challenges from remote sensing perspective, ISPRS J. Photogramm. Remote Sens., 150, 115, 10.1016/j.isprsjprs.2019.02.009 Jia, 2019, A New Vegetation Index to Detect Periodically Submerged Mangrove Forest Using Single-Tide Sentinel-2 Imagery, Remote Sens., 11, 10.3390/rs11172043 Jia, 2021, Rapid, robust, and automated mapping of tidal flats in China using time series Sentinel-2 images and Google Earth Engine, Remote Sens. Environ., 255, 112285, 10.1016/j.rse.2021.112285 Jia, 2018, Monitoring loss and recovery of mangrove forests during 42 years: The achievements of mangrove conservation in China, Int. J. Appl. Earth Obs. Geoinf., 73, 535 Jia, 2015, Landsat-Based Estimation of Mangrove Forest Loss and Restoration in Guangxi Province, China, Influenced by Human and Natural Factors, IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens., 8, 311, 10.1109/JSTARS.2014.2333527 Jia, 2014, Mapping the distribution of mangrove species in the Core Zone of Mai Po Marshes Nature Reserve, Hong Kong, using hyperspectral data and high-resolution data, Int. J. Appl. Earth Obs. Geoinf., 33, 226 Liu, 2017, Monitoring the invasion of Spartina alterniflora using multi-source high-resolution imagery in the Zhangjiang Estuary China, Remote Sens., 9, 539, 10.3390/rs9060539 Liu, 2022, Remote Sensing Based Conservation Effectiveness Evaluation of Mangrove Reserves in China, Remote Sens., 14, 1386, 10.3390/rs14061386 Löw, 2013, Impact of feature selection on the accuracy and spatial uncertainty of per-field crop classification using Support Vector Machines, ISPRS J. Photogramm. Remote Sens., 85, 102, 10.1016/j.isprsjprs.2013.08.007 Lu, 2018, Dynamic Analysis of Mangrove Forests Based on an Optimal Segmentation Scale Model and Multi-Seasonal Images in Quanzhou Bay China, Remote Sens., 10, 2020, 10.3390/rs10122020 Luo, 2020, Mapping Long-Term Spatiotemporal Dynamics of Pen Aquaculture in a Shallow Lake: Less Aquaculture Coming along Better Water Quality, Remote Sens., 12, 1866, 10.3390/rs12111866 Mao, 2020, National wetland mapping in China: A new product resulting from object-based and hierarchical classification of Landsat 8 OLI images, ISPRS J. Photogramm. Remote Sens., 164, 11, 10.1016/j.isprsjprs.2020.03.020 Mondal, 2019, Evaluating Combinations of Sentinel-2 Data and Machine-Learning Algorithms for Mangrove Mapping in West Africa, Remote Sens., 11, 2928, 10.3390/rs11242928 Oreopoulos, 2011, Implementation on Landsat data of a simple cloud-mask algorithm developed for MODIS land bands, IEEE Geosci. Remote Sens. Lett., 8, 597, 10.1109/LGRS.2010.2095409 Otsu, 1979, A threshold selection method from gray-level histograms, IEEE Trans. Syst. Man Cybern., 9, 62, 10.1109/TSMC.1979.4310076 Pham, 2017, Monitoring mangrove biomass change in Vietnam using SPOT images and an object-based approach combined with machine learning algorithms, ISPRS J. Photogramm. Remote Sens., 128, 86, 10.1016/j.isprsjprs.2017.03.013 Tian, 2017, Comparison of UAV and WorldView-2 imagery for mapping leaf area index of mangrove forest, Int. J. Appl. Earth Obs. Geoinf., 61, 22 Tian, 2020, Development of spectral-phenological features for deep learning to understand Spartina alterniflora invasion, Remote Sens. Environ., 242, 10.1016/j.rse.2020.111745 Wang, 2018, Evaluating the performance of Sentinel-2, Landsat 8 and Pléiades-1 in mapping mangrove extent and species, Remote Sens., 10, 1468, 10.3390/rs10091468 Wang, 2018, Artificial Mangrove Species Mapping Using Pléiades-1: An Evaluation of Pixel-Based and Object-Based Classifications with Selected Machine Learning Algorithms, Remote Sensing, 10, 294, 10.3390/rs10020294 Wang, 2019, A review of remote sensing for mangrove forests: 1956–2018, Remote Sens. Environ., 231 Wang, 2004, Comparison of IKONOS and QuickBird images for mapping mangrove species on the Caribbean coast of Panama, Remote Sens. Environ., 91, 432, 10.1016/j.rse.2004.04.005 Wang, 2020, Can Strict Protection Stop the Decline of Mangrove Ecosystems in China? From Rapid Destruction to Rampant Degradation, Forests, 11, 55, 10.3390/f11010055 Xu, 2011, Characteristic analysis of Otsu threshold and its applications, Pattern Recogn. Lett., 32, 956, 10.1016/j.patrec.2011.01.021 Yang, 2022, Enhanced mangrove vegetation index based on hyperspectral images for mapping mangrove, ISPRS J. Photogramm. Remote Sens., 189, 236, 10.1016/j.isprsjprs.2022.05.003 Yin, 2019, Individual mangrove tree measurement using UAV-based LiDAR data: Possibilities and challenges, Remote Sens. Environ., 223, 34, 10.1016/j.rse.2018.12.034 You, 2021, The 10-m crop type maps in Northeast China during 2017–2019, Sci. Data, 8, 1, 10.1038/s41597-021-00827-9 Zafari, 2019, Evaluating the performance of a random forest kernel for land cover classification, Remote Sens., 11, 575, 10.3390/rs11050575 Zeng, 2022, Optical vegetation indices for monitoring terrestrial ecosystems globally, Nat. Rev. Earth Environ., 3, 477, 10.1038/s43017-022-00298-5 Zhang, 2021, a fine-scale mangrove map of China derived from 2-meter resolution satellite observations and field data, ISPRS Int. J. Geo-Inf., 10, 92, 10.3390/ijgi10020092 Zhang, 2021, A Comparison of Gaofen-2 and Sentinel-2 Imagery for Mapping Mangrove Forests Using Object-Oriented Analysis and Random Forest, IEEE J. Sel. Top. Appl. Earth Observ. Remote Sens., 14, 4185, 10.1109/JSTARS.2021.3070810 Zheng, 2021, Assessing the ecological vulnerability of protected areas by using Big Earth Data, Int. J. Digital Earth, 14, 1624, 10.1080/17538947.2021.1962995 Zhou, 2015, Use of exotic plants to control Spartina alterniflora invasion and promote mangrove restoration, Sci. Rep., 5, 1 Zhu, 2021, Combining time series and land cover data for analyzing spatio-temporal changes in mangrove forests: A case study of Qinglangang Nature Reserve, Hainan China, Ecol. Indicators, 131, 108135, 10.1016/j.ecolind.2021.108135