Abstract
Abstract. 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 Bruguiera gymnorrhiza and small Aegiceras corniculatum are distinguishable at 73–100% accuracy, whereas tall A. corniculatum was correctly classified at only 53% due to its mixed vegetation stands with B. gymnorrhiza (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. Kandelia obovata) 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.
Highlights
Mangroves provide a wide array of ecological and economic benefits (Dahdouh-Guebas and Koedam, 2006a; DahdouhGuebas et al, 2006a; Nagelkerken et al, 2008; Walters et al, 2008)
Mangrove denudation at the rate of 2.1 % annually is much higher than the loss of tropical forests and coral reefs (Valiela et al, 2001). This constant pressure on mangrove ecosystems underlines the demand for mangrove biogeographical data and vegetation maps that can be used by local authorities for better conservation and management practices (Masso i Aleman et al, 2010)
The study was conducted near Gaoqiao in southern China (Fig. 1), where the mangroves are managed by the Zhanjiang Mangrove National Nature Reserve (ZMNNR)
Summary
Mangroves provide a wide array of ecological and economic benefits (Dahdouh-Guebas and Koedam, 2006a; DahdouhGuebas et al, 2006a; Nagelkerken et al, 2008; Walters et al, 2008). Mangrove denudation at the rate of 2.1 % annually is much higher than the loss of tropical forests and coral reefs (Valiela et al, 2001) This constant pressure on mangrove ecosystems underlines the demand for mangrove biogeographical data and vegetation maps (produced at species level and areal extent) that can be used by local authorities for better conservation and management practices (Masso i Aleman et al, 2010). The coarse resolution of most remote sensors (e.g. Landsat, SPOT, etc.) provides enough information to discriminate mangroves at the species level but only for large and homogeneous stands In mangrove research, both spatial and spectral resolution need to be high to enable delineation of small patch size of certain species. Remote sensing, combined with ground-truth observations in a GIS environment, remains time-saving as well as cost-effective for qualitative and quantitative assessment of the mangrove vegetation (Dahdouh-Guebas, 2002; Dahdouh-Guebas et al, 2006b; Satyanarayana et al, 2011; Green et al, 2000)
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