Abstract
Mangroves have significant social, economic, environmental, and ecological values but they are under threat due to human activities. An accurate map of mangrove species distribution is required to effectively conserve mangrove ecosystem. This study evaluates the synergy of WorldView-3 (WV-3) spectral bands and high return density LiDAR-derived elevation metrics for classifying seven species in mangrove habitat in Mai Po Nature Reserve in Hong Kong, China. A recursive feature elimination algorithm was carried out to identify important spectral bands and LiDAR (Airborne Light Detection and Ranging) metrics whilst appropriate spatial resolution for pixel-based classification was investigated for discriminating different mangrove species. Two classifiers, support vector machine (SVM) and random forest (RF) were compared. The results indicated that the combination of 2 m resolution WV-3 and LiDAR data yielded the best overall accuracy of 0.88 by SVM classifier comparing with WV-3 (0.72) and LiDAR (0.79). Important features were identified as green (510–581 nm), red edge (705–745 nm), red (630–690 nm), yellow (585–625 nm), NIR (770–895 nm) bands of WV-3, and LiDAR metrics relevant to canopy height (e.g., canopy height model), canopy shape (e.g., canopy relief ratio), and the variation of height (e.g., variation and standard deviation of height). LiDAR features contributed more information than spectral features. The significance of this study is that a mangrove species distribution map with satisfactory accuracy can be acquired by the proposed classification scheme. Meanwhile, with LiDAR data, vertical stratification of mangrove forests in Mai Po was firstly mapped, which is significant to bio-parameter estimation and ecosystem service evaluation in future studies.
Highlights
Mangrove forests are unique inter-tidal wetland in the tropical and subtropical coastal areas that have significant value to human society and living environment
The objectives of this study are (1) to accurately map the mangrove species distribution in the core zone of Mai Po Nature Reserve, (2) to identify and reveal spectral bands and Light Detection and Ranging (LiDAR) elevation metrics that are important for distinguishing mangrove species, (3) to investigate the appropriate image resolution and LiDAR metrics grid size for a pixel-based classification, and (4) to evaluate the performance of different classifiers in the proposed classification scheme
When the LiDAR elevation metrics were input for feature selection, the results showed a slight difference between the 2 m-grid metrics versus the 5 m-grid metrics. 10 metrics were retained from
Summary
Mangrove forests are unique inter-tidal wetland in the tropical and subtropical coastal areas that have significant value to human society and living environment. In terms of social and economic value, mangrove forest has high primary productivity which on one hand, provides habitat for diverse aquatic plants, animals and attracts thousands of water birds and maintains bio-diversity. With respect to environmental and ecological values, mangrove forest protects the shoreline from erosion because of tide, wind, and storms, and acts as the first defensive line to extreme weather for coastal areas. The mangrove ecosystem is a valuable resource for recreation, education, and scientific research [1]. Mangrove habitat is threatened around the world due to human activities. Understanding the mangrove habitat is crucial for environmental conservation and an accurate map of species distribution is required to this end
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