Mangrove forests change analysis in the western coastal of Guangdong Province, China using remote sensing and GIS (1988–2008)

Mangroves are small evergreen trees that flourish in the intertidal zones of river deltas, lagoons, estuaries, and coastal systems in the tropics, subtropics, and some temperate coasts. Mangroves provide a variety of ecological services for human beings, such as the protection of coasts from typhoon damage, pollutant absorption, and water purification. Guangdong province, used to locate half of mangrove wetlands in China, has witnessed the rapid decrease of the mangrove wetland since reform and opening up in late 1970s. Remote sensing can be conveniently used for the inventory of mangrove forests because field investigation is very difficult to carry out. Multi-temporal remote sensing images can also used to generate a long term spatial distribution and change detection of nature resource or land covers. In this study, a time series of Landsat TM and ETM+ images were used to extract mangrove forests change information in the Pearl River Estuary and the western coastal of Guangdong Province in South China over the past 20 years. Three time period (1988–1991, 1998–2000, and 2006–2008) mangrove maps were generated from these Landsat images. Comparison of classification results between theses three time periods shows that the geographic coverage of mangrove forests has dramatic change both in area and spatial distribution in the past three decades in the Pearl River Estuary and the western coastal of Guangdong Province. This study documents the changes of mangrove forests and demonstrates that Remote Sensing and GIS offer important data and tools in the advancement of coastal nature resource managements and ecosystem monitoring. Application of Geographical Information Sciences technologies and the change information are critical for evaluating, management, protection these precious nature resources, and decision making for sustainable development in local government.

[1]  N. Dessay,et al.  Mangrove mapping in North-Western Madagascar using SPOT-XS and SIR-C radar data , 1999, Hydrobiologia.

[2]  K. Liu,et al.  Regression and analytical models for estimating mangrove wetland biomass in South China using Radarsat images , 2007 .

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

[4]  C. Vaiphasa Remote sensing techniques for mangrove mapping , 2006 .

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

[6]  R. Maniere,et al.  Remote sensing techniques adapted to high resolution mapping of tropical coastal marine ecosystems (coral reefs, seagrass beds and mangrove) , 1998 .

[7]  Li Xia,et al.  Classification of Mangroves by Data Fusion and Neural Networks , 2006 .

[8]  William De Genst,et al.  High-Resolution Vegetation Data for Mangrove Research as Obtained From Aerial Photography , 2002 .

[9]  F. Blasco,et al.  RECENT ADVANCES IN MANGROVE STUDIES USING REMOTE SENSING DATA , 1998 .

[10]  Zhang Qian-mei,et al.  Mangrove resource and sustainable development at Zhanjiang , 2006 .

[11]  Lin Zhong Protection and Management Countermeasures for Mangrove Resourc e in Guangdong Province , 2003 .

[12]  Li Tian Detection and Analysis of Mangrove Changes with Multi-temporal Remotely Sensed Imagery in the Shenzhen River Estuary , 2002 .

[13]  J. Tobey,et al.  Remote Sensing of Mangrove Change Along the Tanzania Coast , 2003 .

[14]  Zhou Yong-zhang,et al.  Landscape Pattern Analyses on the Mangrove Forest Wetland of Qi'ao Island in the Last Two Decades , 2005 .

[15]  Karen C. Seto,et al.  Mangrove conversion and aquaculture development in Vietnam: A remote sensing-based approach for evaluating the Ramsar Convention on Wetlands , 2007 .

[16]  Anthony Yeh,et al.  Inventory of mangrove wetlands in the Pearl River Estuary of China using remote sensing , 2006 .

[17]  A. Skidmore,et al.  A post-classifier for mangrove mapping using ecological data , 2006 .