Abstract
Mapping mangrove extent and species is important for understanding their response to environmental changes and for observing their integrity for providing goods and services. However, accurately mapping mangrove extent and species are ongoing challenges in remote sensing. The newly-launched and freely-available Sentinel-2 (S2) sensor offers a new opportunity for these challenges. This study presents the first study dedicated to the examination of the potential of original bands, spectral indices, and texture information of S2 in mapping mangrove extent and species in the first National Nature Reserve for mangroves in Dongzhaigang, China. To map mangrove extent and species, a three-level hierarchical structure based on the spatial structure of a mangrove ecosystem and geographic object-based image analysis is utilized and modified. During the experiments, to conquer the challenge of optimizing high-dimension and correlated feature space, the recursive feature elimination (RFE) algorithm is introduced. Finally, the selected features from RFE are employed in mangrove species discriminations, based on a random forest algorithm. The results are compared with those of Landsat 8 (L8) and Pléiades-1 (P1) data and show that S2 and L8 could accurately extract mangrove extent, but P1 obviously overestimated it. Regarding mangrove species community levels, the overall classification accuracy of S2 is 70.95%, which is lower than P1 imagery (78.57%) and slightly higher than L8 data (68.57%). Meanwhile, the former difference is statistically significant, and the latter is not. The dominant species is extracted basically in S2 and P1 imagery, but for the occasionally distributed K. candel and the pioneer and fringe mangrove A. marina, S2 performs poorly. Concerning L8, S2, and P1, there are eight (8/126), nine (9/218), and eight (8/73) features, respectively, that are the most important for mangrove species discriminations. The most important feature overall is the red-edge bands, followed by shortwave infrared, near infrared, blue, and other visible bands in turn. This study demonstrates that the S2 sensor can accurately map mangrove extent and basically discriminate mangrove species communities, but for the latter, one should be cautious due to the complexity of mangrove species.
Highlights
Mangroves are salt-tolerant evergreen woody plants and grow in the inter-tidal region in the tropics and subtropics, distributed in 118 countries and regions, with a total global area of about 137,760 km2 [1]
Though mangroves provide a wide range of ecosystem services and goods, global mangrove forests declined by 35% from 1980 to 2000 due to conversion to agricultural land, aquaculture ponds, and construction land [2,3]
Due to the special growth environment of mangroves and their dense forests, it is very difficult for people to enter mangrove forests for extensive field surveying and sampling
Summary
Mangroves are salt-tolerant evergreen woody plants and grow in the inter-tidal region in the tropics and subtropics, distributed in 118 countries and regions, with a total global area of about 137,760 km2 [1]. They play an important role in windbreaks, shoreline stabilization and maintenance of ecological balance and biodiversity [2]. Mangrove forests in China have been largely damaged, decreasing from 420 km in the 1950s to 220 km by 2000 [4] It is crucial, to monitor the extent and species of mangrove forests against land use change and forest ecosystem degradation. Remote sensing data have been widely used for evaluating mangroves in the past two decades [5,6], such as for mapping the distribution of global mangroves [1], monitoring the extent and dynamics of national [7,8,9] or regional [10,11,12] mangroves, identifying species composition of local mangroves [13,14,15], and estimating biophysical indicators [16,17]
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