BG Biogeosciences BG Biogeosciences 1726-4189 Copernicus Publications Göttingen, Germany 10.5194/bg-10-5681-2013 Dynamics in mangroves assessed by high-resolution and multi-temporal satellite data: a case study in Zhanjiang Mangrove National Nature Reserve (ZMNNR), P. R. China Leempoel K. 7 1 Satyaranayana B. 3 2 1 Bourgeois C. 1 Zhang J. 4 Chen M. 4 Wang J. 5 Bogaert J. 6 Dahdouh-Guebas F. 1 2 Laboratory of Systems Ecology and Resource Management, Département de Biologie des Organismes, Faculté des Sciences, Université Libre de Bruxelles-ULB, Avenue Franklin D. Roosevelt 50, CPI 264/1, 1050 Brussels, Belgium Laboratory of Plant Biology and Nature Management, Mangrove Management Group, Vrije Universiteit Brussel-VUB, Pleinlaan 2, 1050 Brussels, Belgium Institute of Oceanography and Environment (INOS), Universiti Malaysia Terengganu (UMT), 21030 Kuala Terengganu, Malaysia Administration Bureau of Zhanjiang Mangrove National Nature Reserve (ZMNNR), Huguang township, Mazhang District, Zhanjiang City, 524088 Guangdong Province, China Guangdong Ocean University (GDOU), Xiashan District, Zhanjiang City, 524025 Guangdong Province, China Biodiversity and Landscape Unit, Université de Liège, Gembloux Agro Bio Tech, Passage des Déportés 2, 5030 Gembloux, Belgium now at: Laboratory of Systems Ecology and Resource Management, Département de Biologie des Organismes, Faculté des Sciences, Université Libre de Bruxelles-ULB, Avenue Franklin D. Roosevelt 50, CPI 264/1, 1050 Brussels, Belgium 27 08 2013 10 8 5681 5689 Copyright: © 2013 K. Leempoel et al. 2013 This work is licensed under the Creative Commons Attribution 3.0 Unported License. To view a copy of this licence, visit https://creativecommons.org/licenses/by/3.0/ This article is available from https://bg.copernicus.org/articles/10/5681/2013/bg-10-5681-2013.html The full text article is available as a PDF file from https://bg.copernicus.org/articles/10/5681/2013/bg-10-5681-2013.pdf

Mangrove forests are declining across the globe, mainly because of human intervention, and therefore require an evaluation of their past and present status (e.g. areal extent, species-level distribution, etc.) to implement better conservation and management strategies. In this paper, mangrove cover dynamics at Gaoqiao (P. R. China) were assessed through time using 1967, 2000 and 2009 satellite imagery (sensors Corona KH-4B, Landsat ETM+, GeoEye-1 respectively). Firstly, multi-temporal analysis of satellite data was undertaken, and secondly biotic and abiotic differences were analysed between the different mangrove stands, assessed through a supervised classification of a high-resolution satellite image. A major decline in mangrove cover (−36%) was observed between 1967 and 2009 due to rice cultivation and aquaculture practices. Moreover, dike construction has prevented mangroves from expanding landward. Although a small increase of mangrove area was observed between 2000 and 2009 (+24%), the ratio mangrove / aquaculture kept decreasing due to increased aquaculture at the expense of rice cultivation in the vicinity. From the land-use/cover map based on ground-truth data (5 × 5 m plot-based tree measurements) (August–September, 2009) as well as spectral reflectance values (obtained from pansharpened GeoEye-1), both <i>Bruguiera gymnorrhiza</i> and small <i>Aegiceras corniculatum</i> are distinguishable at 73–100% accuracy, whereas tall <i>A. corniculatum</i> was correctly classified at only 53% due to its mixed vegetation stands with <i>B. gymnorrhiza</i> (overall classification accuracy: 85%). In the case of sediments, sand proportion was significantly different between the three mangrove classes. Overall, the advantage of very high resolution satellite images like GeoEye-1 (0.5 m) for mangrove spatial heterogeneity assessment and/or species-level discrimination was well demonstrated, along with the complexity to provide a precise classification for non-dominant species (e.g. <i>Kandelia obovata</i>) at Gaoqiao. Despite limitations such as geometric distortion and single panchromatic band, the 42 yr old Corona declassified images are invaluable for land-use/cover change detections when compared to recent satellite data sets.

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