Distinguishing Original and Non-Original Stands at the Zhanjiang Mangrove National Nature Reserve (P.R. China): Remote Sensing and GIS for Conservation and Ecological Research

• Main Authors: Juan Durango-Cordero, Behara Satyanarayana, Jonathan Cheung-Wai Chan, Jan Bogaert, Farid Dahdouh-Guebas
• Format: Article
• Language: English
• Published: MDPI AG 2021-07-01
• Series: Remote Sensing
• Subjects: spatial distribution; vegetation structure; original forest; remote sensing; GIS
• Online Access: https://www.mdpi.com/2072-4292/13/14/2781

The present research developed a novel methodological framework to differentiate natural mangrove stands (i.e., original), from stands which were planted and stands naturally established after interaction between planted and non-planted stands (e.g., through pollination, i.e., non-original). Ground-truth and remote sensing data were collected for Zhanjiang Mangrove National Nature Reserve (ZMNNR) in P.R. China. First, satellite images of Corona (1967) and GeoEye-1 (2009) were overlaid to identify original (1967) and non-original (2009) mangrove stands. Second, in both stands a total of 75 in situ plots (25 m²) were measured for ground-truthing of tree structural parameters including height, density, basal area and Complexity Index (CI). From temporal satellite data, we identify 236.12 ha of original mangrove and 567.88 ha of non-original mangrove in the reserve. Averaged measurements of the original mangrove stands, i.e., stem density (1164 nos. 0.1 ha⁻¹), basal area (90.3 m² 0.1 ha⁻¹) and CI (100.59), indicated that they were in a state of maturity and less disturbed compared to the non-original mangroves (density, 1241 nos. 0.1 ha⁻¹; basal area, 4.92 m² 0.1 ha⁻¹ and CI, 55.65). The Kruskal–Wallis test showed significant differentiation between the original and non-original mangrove tree structural parameters: <i>Kandelia obovata</i>’s density, <i>X</i>² = 34.78, d.f. = 1, <i>p</i> = 0.001; basal area, <i>X</i>² = 108.15, d.f. = 1, <i>p</i> = 0.001; <i>Rizhopora stylosa</i>’s density, <i>X</i>² = 64.03, d.f. = 1, <i>p</i> = 0.001; basal area, <i>X</i>² = 117.96, d.f. = 1, <i>p</i> = 0.001. The latter is also evident from the clustering plots generated from the Principal Component Analysis (PCA). Vegetation dynamics at the ZMNNR also enabled us to compare the species composition and distribution patterns with other Indo-West Pacific regions. Overall, the present study not only highlights the advantage of >50 years old satellite data but also provide a benchmark for future ecological research, conservation and management of the ZMNNR.