Dynamic Analysis of Mangrove Forests Based on an Optimal Segmentation Scale Model and Multi-Seasonal Images in Quanzhou Bay, China

Mangrove forests are important coastal ecosystems and are crucial for the equilibrium of the global carbon cycle. Monitoring and mapping of mangrove forests are essential for framing knowledge-based conservation policies and funding decisions by governments and managers. The purpose of this study was to monitor mangrove forest dynamics in the Quanzhou Bay Estuary Wetland Nature Reserve. To achieve this goal, we compared and analyzed the spectral discrimination among mangrove forests, mudflats and Spartina using multi-seasonal Landsat images from 1990, 1997, 2005, 2010, and 2017. We identified the spatio-temporal distribution of mangrove forests by combining an optimal segmentation scale model based on object-oriented classification, decision tree and visual interpretation. In addition, mangrove forest dynamics were determined by combining the annual land change area, centroid migration and overlay analysis. The results showed that there were advantages in the approaches used in this study for monitoring mangrove forests. From 1990 to 2017, the extent of mangrove forests increased by 2.48 km2, which was mostly converted from mudflats and Spartina. Environmental threats including climate change and sea-level rise, aquaculture development and Spartina invasion, pose potential and direct threats to the existence and expansion of mangrove forests. However, the implementation of reforestation projects and Spartina control plays a substantial role in the expansion of mangrove forests. It has been demonstrated that conservation activities can be beneficial for the restoration and succession of mangrove forests. This study provides an example of how the application of an optimal segmentation scale model and multi-seasonal images to mangrove forest monitoring can facilitate government policies that ensure the effective protection of mangrove forests.

[1]  Victor H. Rivera-Monroy,et al.  Mangrove forests in a rapidly changing world: Global change impacts and conservation opportunities along the Gulf of Mexico coast , 2018, Estuarine, Coastal and Shelf Science.

[2]  S. Biswas,et al.  Plant invasion in mangrove forests worldwide , 2018, Forest Ecology and Management.

[3]  Chao Wang,et al.  Monitoring loss and recovery of mangrove forests during 42 years: The achievements of mangrove conservation in China , 2018, Int. J. Appl. Earth Obs. Geoinformation.

[4]  Bing Xu,et al.  Monitoring mangrove forest change in China from 1990 to 2015 using Landsat-derived spectral-temporal variability metrics , 2018, Int. J. Appl. Earth Obs. Geoinformation.

[5]  J. Woodruff.  Future of tidal wetlands depends on coastal management , 2018, Nature.

[6]  Dieu Tien Bui,et al.  Willingness to pay for mangrove restoration in the context of climate change in the Cat Ba biosphere reserve, Vietnam , 2018, Ocean & Coastal Management.

[7]  P. Ravichandran,et al.  Impact of shrimp aquaculture development on important ecosystems in India , 2018, Global Environmental Change.

[8]  He Li,et al.  Mapping Mangrove Forests Based on Multi-Tidal High-Resolution Satellite Imagery , 2018, Remote. Sens..

[9]  Dieu Tien Bui,et al.  Estimating aboveground biomass of a mangrove plantation on the Northern coast of Vietnam using machine learning techniques with an integration of ALOS-2 PALSAR-2 and Sentinel-2A data , 2018 .

[10]  R. Sidle,et al.  Using remote sensing and traditional ecological knowledge (TEK) to understand mangrove change on the Maroochy River, Queensland, Australia , 2018 .

[11]  J. Cissell,et al.  Monitoring mangrove forest dynamics in Campeche, Mexico, using Landsat satellite data , 2018 .

[12]  Severino G. Salmo,et al.  Estimation and mapping of above-ground biomass of mangrove forests and their replacement land uses in the Philippines using Sentinel imagery , 2017 .

[13]  Mingming Jia,et al.  Remote sensing investigation of anthropogenic land cover expansion in the low-elevation coastal zone of Liaoning Province, China , 2017 .

[14]  N. Clinton,et al.  A mangrove forest map of China in 2015: Analysis of time series Landsat 7/8 and Sentinel-1A imagery in Google Earth Engine cloud computing platform , 2017 .

[15]  Chen Shi,et al.  Comparison of UAV and WorldView-2 imagery for mapping leaf area index of mangrove forest , 2017, Int. J. Appl. Earth Obs. Geoinformation.

[16]  Shengwei Zhang,et al.  Local and global evaluation for remote sensing image segmentation , 2017 .

[17]  N. Saintilan,et al.  Potential increase in coastal wetland vulnerability to sea-level rise suggested by considering hydrodynamic attenuation effects , 2017, Nature Communications.

[18]  Mingming Jia,et al.  Monitoring the Invasion of Spartina alterniflora Using Multi-source High-resolution Imagery in the Zhangjiang Estuary, China , 2017, Remote. Sens..

[19]  Dieu Tien Bui,et al.  Biomass estimation of Sonneratia caseolaris (l.) Engler at a coastal area of Hai Phong city (Vietnam) using ALOS-2 PALSAR imagery and GIS-based multi-layer perceptron neural networks , 2017 .

[20]  Guirui Yu,et al.  New insight into global blue carbon estimation under human activity in land-sea interaction area: A case study of China , 2016 .

[21]  Mingming Jia,et al.  Evaluating the Effectiveness of Conservation on Mangroves: A Remote Sensing-Based Comparison for Two Adjacent Protected Areas in Shenzhen and Hong Kong, China , 2016, Remote. Sens..

[22]  Clement Atzberger,et al.  Self-Guided Segmentation and Classification of Multi-Temporal Landsat 8 Images for Crop Type Mapping in Southeastern Brazil , 2015, Remote. Sens..

[23]  T. Lihan,et al.  Mapping mangrove changes in the Matang Mangrove Forest using multi temporal satellite imageries , 2015 .

[24]  Zhili Feng,et al.  Use of exotic plants to control Spartina alterniflora invasion and promote mangrove restoration , 2015, Scientific Reports.

[25]  Nicholas Cuba,et al.  Research note: Sankey diagrams for visualizing land cover dynamics , 2015 .

[26]  Bruce W. Pengra,et al.  Distribution and dynamics of mangrove forests of South Asia. , 2015, Journal of environmental management.

[27]  Kaishan Song,et al.  Landsat-Based Estimation of Mangrove Forest Loss and Restoration in Guangxi Province, China, Influenced by Human and Natural Factors , 2015, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[28]  Wenqing Wang,et al.  Recruitment and herbivory affect spread of invasive Spartina alterniflora in China. , 2014, Ecology.

[29]  Huawei Wan,et al.  Monitoring the Invasion of Spartina alterniflora Using Very High Resolution Unmanned Aerial Vehicle Imagery in Beihai, Guangxi (China) , 2014, TheScientificWorldJournal.

[30]  P. Sutton,et al.  Changes in the global value of ecosystem services , 2014 .

[31]  J. Kauffman,et al.  Carbon stocks of intact mangroves and carbon emissions arising from their conversion in the Dominican Republic. , 2014, Ecological applications : a publication of the Ecological Society of America.

[32]  D. Mao,et al.  Mapping China’s mangroves based on an object-oriented classification of Landsat imagery , 2014, Wetlands.

[33]  D. Gruner,et al.  Poleward expansion of mangroves is a threshold response to decreased frequency of extreme cold events , 2013, Proceedings of the National Academy of Sciences.

[34]  G. Lin,et al.  Effects of sea level rise on mangrove Avicennia population growth, colonization and establishment: Evidence from a field survey and greenhouse manipulation experiment , 2013 .

[35]  K. Didan,et al.  Detecting large scale conversion of mangroves to aquaculture with change point and mixed-pixel analyses of high-fidelity MODIS data , 2013 .

[36]  D. Alongi.  Carbon sequestration in mangrove forests , 2012 .

[37]  G. Arturo Sánchez-Azofeifa,et al.  The effect of seasonal spectral variation on species classification in the Panamanian tropical forest , 2012 .

[38]  Guanghui Lin,et al.  Interactions between mangroves and exotic Spartina in an anthropogenically disturbed estuary in southern China. , 2012, Ecology.

[39]  Steffen Gebhardt,et al.  Remote Sensing of Mangrove Ecosystems: A Review , 2011, Remote. Sens..

[40]  Kecheng Zhang,et al.  The Role of the Everglades Mangrove Ecotone Region (EMER) in Regulating Nutrient Cycling and Wetland Productivity in South Florida , 2011 .

[41]  Benjamin W. Heumann.  Satellite remote sensing of mangrove forests: Recent advances and future opportunities , 2011 .

[42]  Ashbindu Singh,et al.  Status and distribution of mangrove forests of the world using earth observation satellite data , 2011 .

[43]  Keming Ma,et al.  Wetland loss under the impact of agricultural development in the Sanjiang Plain, NE China , 2010, Environmental monitoring and assessment.

[44]  Dirk Tiede,et al.  ESP: a tool to estimate scale parameter for multiresolution image segmentation of remotely sensed data , 2010, Int. J. Geogr. Inf. Sci..

[45]  Tsai-Ming Lee,et al.  Applying remote sensing techniques to monitor shifting wetland vegetation: a case study of Danshui River estuary mangrove communities, Taiwan. , 2009 .

[46]  Xun Shi,et al.  Monitoring mangrove forest changes using remote sensing and GIS data with decision-tree learning , 2008, Wetlands.

[47]  M. Soares.  A Conceptual Model for the Responses of Mangrove Forests to Sea Level Rise , 2009 .

[48]  Maurizio Mencuccini,et al.  Below-ground root yield and distribution in natural and replanted mangrove forests at Gazi bay, Kenya , 2008 .

[49]  P. Mayaux,et al.  An object-based method for mapping and change analysis in mangrove ecosystems , 2008 .

[50]  Le Wang,et al.  Photogrammetric Engineering & Remote Sensing Neural Network Classification of Mangrove Species from Multi-seasonal Ikonos Imagery , 2022 .

[51]  M. Simard,et al.  A systematic method for 3D mapping of mangrove forests based on Shuttle Radar Topography Mission elevation data, ICEsat/GLAS waveforms and field data: Application to Ciénaga Grande de Santa Marta, Colombia , 2008 .

[52]  Chandra Giri,et al.  Mangrove forest distributions and dynamics (1975–2005) of the tsunami‐affected region of Asia † , 2008 .

[53]  Tan Fang-lin.  SPARTINA CONTROL STRATEGY OF CHINA , 2007 .

[54]  Chung-Hsin Chung,et al.  Forty years of ecological engineering with Spartina plantations in China , 2006 .

[55]  Antônio Miguel Vieira Monteiro,et al.  Parameter selection for region‐growing image segmentation algorithms using spatial autocorrelation , 2006 .

[56]  Hélène Laurent,et al.  Unsupervised Performance Evaluation of Image Segmentation , 2006, EURASIP J. Adv. Signal Process..

[57]  Kerrylee Rogers,et al.  Mangrove encroachment of salt marsh in Western Port Bay, Victoria: The role of sedimentation, subsidence, and sea level rise , 2005 .

[58]  D. Alongi.  Present state and future of the world's mangrove forests , 2002, Environmental Conservation.

[59]  C. Visvanathan,et al.  Environmental Issues in Brackish Water Shrimp Aquaculture in Sri Lanka , 2001, Environmental management.

[60]  K. Kathiresan,et al.  Biology of mangroves and mangrove Ecosystems , 2001 .

[61]  Arno Schäpe,et al.  Multiresolution Segmentation : an optimization approach for high quality multi-scale image segmentation , 2000 .

[62]  Russell G. Congalton,et al.  Assessing the accuracy of remotely sensed data : principles and practices , 1998 .