Abstract
Due to continuous degradation of mangrove forests, the accurate monitoring of spatial distribution and species composition of mangroves is essential for restoration, conservation and management of coastal ecosystems. With leaf hyperspectral reflectance, this study aimed to explore the potential of continuous wavelet analysis (CWA) combined with different sample subset partition (stratified random sampling (STRAT), Kennard-Stone sampling algorithm (KS), and sample subset partition based on joint X-Y distances (SPXY)) and feature extraction methods (principal component analysis (PCA), successive projections algorithm (SPA), and vegetation index (VI)) in mangrove species classification. A total of 301 mangrove leaf samples with four species (Avicennia marina, Bruguiera gymnorrhiza, Kandelia obovate and Aegiceras corniculatum) were collected across six different regions. The smoothed reflectance (Smth) and first derivative reflectance (Der) spectra were subjected to CWA using different wavelet scales, and a total of 270 random forest classification models were established and compared. Among the 120 models with CWA of Smth, 88.3% of models increased the overall accuracy (OA) values with an improvement of 0.2–28.6% compared to the model with the Smth spectra; among the 120 models with CWA of Der, 25.8% of models increased the OA values with an improvement of 0.1–11.4% compared to the model with the Der spectra. The model with CWA of Der at the scale of 23 coupling with STRAT and SPA achieved the best classification result (OA = 98.0%), while the best model with Smth and Der alone had OA values of 86.3% and 93.0%, respectively. Moreover, the models using STRAT outperformed those using KS and SPXY, and the models using PCA and SPA had better performances than those using VIs. We have concluded that CWA with suitable scales holds great potential in improving the classification accuracy of mangrove species, and that STRAT combined with the PCA or SPA method is also recommended to improve classification performance. These results may lay the foundation for further studies with UAV-acquired or satellite hyperspectral data, and the encouraging performance of CWA for mangrove species classification can also be extended to other plant species.
Highlights
Mangrove forests are communities of diverse salt-tolerant evergreen trees and other plant species in tropical and subtropical intertidal zones, and they provide important ecosystem services such as nutrient cycling, carbon sequestration, and coastal hazard mitigation [1,2,3,4]
With the leaf hyperspectral reflectance spectra of four mangrove species samples collected across six regions, this study aimed to explore the potential of continuous wavelet analysis (CWA) combined with different sample subset ppaarrttiittiioonnaannddfefeaatuturereexetxrtarcatciotinonmmethetohdosdisn imnamngarnogvreovspeescpieescicelasscsliafiscsaitfiiocant.ioTnh.eTrehseurltessautlttsheatletahfescleaalef mscaaylelmayaythleayfotuhnedfoatuionndaftoironfufrothr efur rstthuedrisetsuwdiieths wUAithV-UaAndV-saatnedllistaet-eblalisteed-bhaysepderhsyppecetrrsapleicmtraagleims. ages
We have explored the potential of CWA combined with different sample subset partition and feature extraction methods in mangrove species classification, and study results have demonstrated that CWA of smoothed reflectance (Smth) and derivative reflectance (Der) spectra with suitable wavelet scales could greatly improve the classification accuracy compared to Smth or Der spectra alone (Table 3)
Summary
Mangrove forests are communities of diverse salt-tolerant evergreen trees and other plant species in tropical and subtropical intertidal zones, and they provide important ecosystem services such as nutrient cycling, carbon sequestration, and coastal hazard (e.g., shoreline erosion, soil salinization, hurricanes, and tsunamis) mitigation [1,2,3,4]. The accurate classification of mangrove species and in-time monitoring of their spatial distribution are critical for conserving and restoring mangrove forests. Medium-resolution multispectral imagery, such as that from Landsat [11,12], SPOT [13], and Sentinel-2 [8] are often used to map the distribution of mangrove forests at regional or even national or global scales. Due to the advantage of their having superb spatial and textural features and high-resolution multispectral images, satellites such as Quickbird [14], IKONOS [15], Worldview [16,17], and Pléiades-1 [18] have been widely employed to classify mangrove species at landscape or regional scales
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