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Open AccessArticle

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

by Chunyan Lu 1,2,3,4, Jinfu Liu 1,3,4, Mingming Jia 2,*, Mingyue Liu 5, Weidong Man 5, Weiwei Fu 1,3,4, Lianxiu Zhong 1,2,3,4, Xiaoqing Lin 1,2,3,4, Ying Su 1,3,4 and Yibin Gao 1,3,4
1
College of Computer and Information Sciences, Fujian Agriculture and Forestry University, Fuzhou 350002, China
2
Key Laboratory of Wetland Ecology and Environment, Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun 130102, China
3
Key Laboratory of Ecology and Resources Statistics of Fujian Province Universities, Fujian Agriculture and Forestry University, Fuzhou 350002, China
4
Research Centre of Resource and Environment Spatial Information Statistics of Fujian Province, Fujian Agriculture and Forestry University, Fuzhou 350002, China
5
College of Mining Engineering, North China University of Science and Technology, Tangshan 063210, China
*
Author to whom correspondence should be addressed.
Remote Sens. 2018, 10(12), 2020; https://doi.org/10.3390/rs10122020
Received: 25 October 2018 / Revised: 1 December 2018 / Accepted: 8 December 2018 / Published: 12 December 2018
(This article belongs to the Special Issue Remote Sensing of Mangroves)
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. View Full-Text
Keywords: mangrove forests; object-oriented classification; optimal segmentation scale model; multi-seasonal image; Quanzhou Bay; remote sensing dynamic monitoring mangrove forests; object-oriented classification; optimal segmentation scale model; multi-seasonal image; Quanzhou Bay; remote sensing dynamic monitoring
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Lu, C.; Liu, J.; Jia, M.; Liu, M.; Man, W.; Fu, W.; Zhong, L.; Lin, X.; Su, Y.; Gao, Y. Dynamic Analysis of Mangrove Forests Based on an Optimal Segmentation Scale Model and Multi-Seasonal Images in Quanzhou Bay, China. Remote Sens. 2018, 10, 2020.

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