Mangrove Species Classification Research Articles

Mangrove species classification using novel adaptive ensemble learning with multi-spatial-resolution multispectral and full-polarization SAR images

ABSTRACT Mangroves are one of the important components of Earth's carbon sinks. The current problems of base-model composition strategy of ensemble learning and image features combination are still major challenges in mangrove species classification. This paper constructed two novel adaptive ensemble learning frameworks (AME-EL and AOS-EL) to explored the effect of combing different spatial-resolution optical and SAR images on classification performance, and evaluated the ability in mangrove species classification between dual-polarization and full-polarization SAR images. Finally, we used the SHAP method to explore the effects of different feature interactions on mangrove species classification. The results indicated that: (1) AME-EL and AOS-EL achieve the fine classification of mangrove species with overall accuracies between 77.50% and 94.77%. (2) Combination of Gaofen-7 multispectral and Gaofen-3 SAR improved the classification accuracy for Kandelia candel, with the F1 score increasing from 26.4% to 40.2%. (3) The VV/VH polarization performed better in the classification, with the F1 scores for Aegiceras corniculatum and Kandelia candel were higher than those of HH/HV and AHV polarization by 7%−16.1% and 5.9%−16.1%, respectively. (4) SAR features interacted well with other spectral features, which made a strong contribution to the classification accuracy of mangrove species, and effectively affect the prediction results.

Performance of XGBoost Ensemble Learning Algorithm for Mangrove Species Classification with Multi-Source Spaceborne Remote Sensing Data

Performance of XGBoost Ensemble Learning Algorithm for Mangrove Species Classification with Multi-Source Spaceborne Remote Sensing Data

Species Classification and Carbon Stock Assessment of Mangroves in Qi’ao Island with Worldview-3 Imagery

Mangroves play a substantial role in the global carbon cycle and are highly productive. To evaluate the effectiveness of a remote-sensing image in mangrove-species classification and carbon stock assessment, we utilized Worldview-3 images to map the mangrove species in Qi’ao Island, Guangdong Province, China, using a Random Forest classifier. We compared the contribution of spectral features, derivation features, and textural features to the classification accuracy and found that textural features significantly improved the overall accuracy, achieving 92.44% with all features combined. According to field-survey results, the main mangrove species in Qi’ao Island were Sonneratia apetala (SA), Acanthus ilicifolius (AI), Kandelia candel (KC), Acrostichum aureum (AA), Aegiceras corniculatum (AC), and Heritiera littoralis (HL); there are also many reeds mixed with mangroves. According to classification results, the total area of the mangroves and reeds is about 451.86 ha; the SA was the dominant species with an area of 393.90 ha. We calculated the total carbon stock of mangroves on Qi’ao Island by integrating the area of different species and their average total carbon density for the first time. The total carbon stock of mangroves in Qi’ao Island is between 147.78–156.14 kt, which demonstrates the significant potential of mangroves in carbon sequestration.

Coupling UAV Hyperspectral and LiDAR Data for Mangrove Classification Using XGBoost in China’s Pinglu Canal Estuary

The fine classification of mangroves plays a crucial role in enhancing our understanding of their structural and functional aspects which has significant implications for biodiversity conservation, carbon sequestration, water quality enhancement, and sustainable development. Accurate classification aids in effective mangrove management, protection, and preservation of coastal ecosystems. Previous studies predominantly relied on passive optical remote sensing images as data sources for mangrove classification, often overlooking the intricate vertical structural complexities of mangrove species. In this study, we address this limitation by incorporating unmanned aerial vehicle-LiDAR (UAV-LiDAR) point cloud 3D data with UAV hyperspectral imagery to perform multivariate classification of mangrove species. Five distinct variable scenarios were employed: band characteristics (S1), vegetation index (S2), texture measures (S3), fused hyperspectral characteristics (S4), and a canopy height model (CHM) combined with UAV hyperspectral characteristics and LiDAR point cloud data (S5). To execute this classification task, an extreme gradient boosting (XGBoost) machine learning algorithm was employed. Our investigation focused on the estuary of the Pinglu Canal, situated within the Maowei Sea of the Beibu Gulf in China. By comparing the classification outcomes of the five variable scenarios, we assessed the unique contributions of each variable to the accurate classification of mangrove species. The findings underscore several key points: (1) The fusion of multiple features in the image scenario led to a higher overall accuracy (OA) compared to models that employed individual features. Specifically, scenario S4 achieved an OA of 88.48% and scenario S5 exhibited an even more impressive OA of 96.78%. These figures surpassed those of the individual feature models where the results were S1 (83.35%), S2 (83.55%), and S3 (71.28%). (2) Combining UAV hyperspectral and LiDAR-derived CHM data yielded improved accuracy in mangrove species classification. This fusion ultimately resulted in an OA of 96.78% and kappa coefficient of 95.96%. (3) Notably, the incorporation of data from individual bands and vegetation indices into texture measures can enhance the accuracy of mangrove species classification. The approach employed in this study—a combination of the XGBoost algorithm and the integration of UAV hyperspectral and CHM features from LiDAR point cloud data—proved to be highly effective and exhibited strong performance in classifying mangrove species. These findings lay a robust foundation for future research efforts focused on mangrove ecosystem services and ecological restoration of mangrove forests.

Examination of the performance of ASEL and MPViT algorithms for classifying mangrove species of multiple natural reserves of Beibu Gulf, south China

Mangrove is the highest productive ecosystems in the global coastal zone, which has high blue carbon sink function and carbon neutrality potential. Fine species classification is essential for mangrove conservation and sustainable development, and has attracted much attention in recent years using ensemble learning and multi-dimensional data. However, the current mangrove species classification based on traditional stacking ensemble learning still faces challenges due to the correlation between base classifiers, differences in meta-classifier capabilities, the subjectivity of parameter tuning, and data redundancy. To address these issues, this paper utilized unmanned aerial vehicle (UAV) multispectral images of three mangrove nature reserves in Beibu Gulf, south China, to examine the classification and generalization ability of our proposed Adaptive Stacking Ensemble Learning (ASEL) algorithm for different mangrove species. We also aim to verify the feasibility of the Multi-Path Vision Transformer for Dense Prediction (MPViT) algorithm for mangrove species mapping, and compare its performance with the ASEL algorithm for mangrove species classification. Finally, we used the SHapley Additive Explanations (SHAP) method to measure the contribution of feature variables to the model, exploring the sensitivity of different image features to mangrove species mapping. This study highlights that: (1) The two ASEL algorithms achieved high accuracy classification of mangrove species with the overall classification accuracy ranging from 79.8% to 96.2%. The ACE-Stacking and AOM-Stacking algorithms performed better classification ability than the traditional stacking algorithm, with the mean overall classification accuracy increasing from 0.9% to 3.3%. The McNemar test further indicated that the differences in classification results derived from three algorithms were significant at the 95% confidence interval, demonstrating the better classification and generalization ability of the ASEL algorithm. (2) The MPViT algorithm achieved better classification accuracy in the three reserves, with an overall accuracy of 95.5%-97.3%, which was 0.6%-5.4% higher than the two ASEL algorithms. The average accuracy of identifying mangrove species was over 95.3% in all reserves, demonstrating the desirable mangrove classification performance of MPViT algorithm. (3) The identification accuracies (F1 scores) of mangrove species in different reserves were ranged from 0.848 to 0.984. Cyperus malaccensi had the highest identification accuracy. (4) The SHAP method interpreted the great contribution of digital surface model and variable atmospherically resistant index features on mangrove species classification. The red band and ratio vegetation index was sensitive to Aegiceras corniculatum and to Cyperus malaccensi.

Quantifying scattering characteristics of mangrove species from Optuna-based optimal machine learning classification using multi-scale feature selection and SAR image time series

Mangroves play a significant role in carbon sequestration and storage. Mapping mangrove species and monitoring their conditions have been a crucial issue for achieving sustainable development goals. Currently combing multidimensional optical and SAR images with machine learning have become an important approach for mangrove species classification, but there are still some challenges in feature selection and hyperparameter optimizations. In this study, we proposed a novel classification framework by combing multi-scale variable selection algorithm (MUVR) with state-of-the-art machine learning hyperparameter optimization method (Optuna) for mapping mangrove species in the Beilun Estuary and Maowei Sea nature reserves using optical and dual-polarization SAR images, and further quantified the scattering characteristics of mangrove species using SAR image time series. We found that: (1) The MUVR algorithm could determine the optimal scale features for different scenarios and mangrove species, and improve the classification performance of machine learning with an overall accuracy (OA) improvement of 12.85%; (2) The Optuna-based optimal CatBoost outperforms LightGBM and NGBoost algorithms in mapping mangrove species, which achieved the highest OA (93.18%). This study demonstrated that LightGBM was suitable for identifying Aegiceras corniculatum, while the CatBoost algorithm was suitable for discriminating Avicennia marina, Bruguiera gymnorrhiza, Cyperus malaccensis, Kandelia candel and Sonneratia apetala; (3) SAR images and its derivatives improved identification ability of mangrove species, and collaboration of multispectral images and SAR-derived features produced the better classification; (4) From 2018 to 2020, the backscattering coefficients of mangrove species in VV and VH polarization focused on 0.053–0.327 and 0.015–0.062, respectively. The coherence coefficients of mangroves displayed a seasonal change trend with the large variations in summer and small variations in winter. The range of Entropy and Alpha of mangrove species was from 0.65 to 0.88 and 17–33, which indicated that the main scattering mechanism of mangroves was moderate random surface scattering.

Performance Evaluation of Mangrove Species Classification Based on Multi-Source Remote Sensing Data Using Extremely Randomized Trees in Fucheng Town, Leizhou City, Guangdong Province

Mangroves are an important source of blue carbon that grow in coastal areas. The study of mangrove species distribution is the basis of carbon storage research. In this study, we explored the potential of combining optical (Gaofen-1, Sentinel-2, and Landsat-9) and fully polarized synthetic aperture radar data from different periods (Gaofen-3) to distinguish mangrove species in the Fucheng town of Leizhou, Guangdong Province. The Gaofen-1 data were fused with Sentinel-2 and Landsat-9 satellite data, respectively. The new data after fusion had both high spatial and spectral resolution. The backscattering coefficient and polarization decomposition parameters of the fully polarized SAR data which could characterize the canopy structure of mangroves were extracted. Ten different feature combinations were designed by combining the two types of data. The extremely randomized trees algorithm (ERT) was used to classify the species, and the optimal feature subset was selected by the feature selection algorithm on the basis of the ERT, and the importance of the features was sorted. Studies show the following: (1) When controlling a single variable, the higher the spatial resolution of the multi-spectral data, the higher the interspecific classification accuracy. (2) The coupled Sentinel-2 and Landsat-9 data with a 2 m resolution will have higher classification accuracy than a single data source. (3) The selected feature subset contains all types of features in the optical data and the polarization decomposition features of the SAR data from different periods: multi-spectral band > texture feature > polarization decomposition parameter > vegetation index. Among the optimized feature combinations, the classification accuracy of mangrove species was the highest, the overall classification accuracy was 90.13%, and Kappa was 0.84, indicating that multi-source and SAR data from different periods coupling could improve the discrimination of mangrove species. (4) The ERT classification algorithm is suitable for the study of mangrove species classification, and the classification accuracy of extremely random trees in this paper is higher than that of random forest (RF), K-nearest neighbor (KNN), and Bayesian (Bayes). The results can provide technical guidance and data support for mangrove species monitoring based on multi-source satellite data.

Retrieval of Mangrove leaf area index and its response to typhoon based on WorldView-3 image

In recent decades, mangroves have become one of the world's most threatened and vulnerable ecosystems due to human disturbance and climate change. Remote sensing techniques have helped to obtain biophysical parameters of forests on a large scale. However, to our knowledge, mangrove leaf area index (LAI) inversion using texture features and its scale dependency is rarely discussed. Whether the dynamics of remotely sensed LAI could reveal the responses of mangrove species to typhoon disturbance remain unknown. In this study, we classified mangrove species, estimated the LAI of each species based on high-resolution WorldView-3 (WV-3) images, and evaluated the performance of different object-based classifiers and the effects of texture features and scale dependency on the LAI model. We characterized typhoon disturbance on different species using interannual LAI variation. Random forest (RF) has the highest accuracy in mangrove species classification, with an overall accuracy of 82.29% after post-classification, compared with naive Bayes (NB), classification and regression trees (CART), k-nearest neighbor (KNN), and support vector machines (SVM). Texture features significantly increased the accuracy of LAI estimates, and the optimal prediction result was generated at a 2-m resampling rate (R2 = 0.52; MSE = 0.56). The mean LAIs of B. gymnorrhiza and S. apetala assemblages were significantly higher than those of K. obovata, S. caseolaris, and A. marina. LAI variation demonstrates a disturbance pattern caused by Typhoon Mangkhut. This typhoon had the greatest impact on K. obovata, while S. apetala and S. caseolaris showed good resistance to wind damage. We concluded that RF was the best classifier for mapping mangrove species distribution using object-based classification algorithms with WV-3 image. The texture features play a key role in constructing the estimation model of mangrove LAI with scale dependency.

Advances in multi- and hyperspectral remote sensing of mangrove species: A synthesis and study case on airborne and multisource spaceborne imagery

This study summarizes the advances in mangrove species mapping based on multispectral and hyperspectral imagery achieved over the last decade. The influence of species diversity and sensor specifications on the performances of various classification approaches are discussed. Based on the limitations of previous approaches, we propose a novel framework to map mangrove species at medium, high, and very-high spatial resolution using multispectral and hyperspectral images. The framework relies on a multitask convolutional neural network to achieve accurate classification at pixel and object (i.e. individual tree crown) level. It was successfully applied to the most comprehensive dataset of optical imagery ever collected over a mangrove forest, achieving accurate mapping of species on airborne (OA = 95 %, Kappa = 0.93) and spaceborne imagery (OA and Kappa up to 97 % and 0.95, respectively), including the newly-operating hyperspectral DESIS and PRISMA instruments. This study brings new insights into the role of spatial and spectral resolutions in mangrove species classification, particularly the importance of short-wave infrared bands. Multispectral imagery performs well at very-high to high resolutions (up to 10–20 m) but suffers from the lack of spectral information at medium resolution (30 m). Hyperspectral imagery provided the best results at sub-metric resolution (0.5 m) and satisfactory results at 30-m resolution (OA ≥ 85), and could benefit from implementing spectral unmixing in the latter case. Given the increasing attention accorded to mangroves, remote sensing will undoubtedly become an essential tool to monitor the diversity of these endangered ecosystems in the future. In that respect, efforts should concentrate on evaluating the capabilities of new and upcoming instruments and multisource data combination to improve mangrove species mapping and, more broadly, to meet conservation goals.

Mapping mangrove species using combined UAV-LiDAR and Sentinel-2 data: Feature selection and point density effects

Mapping mangrove species is important for mangrove conservation and rehabilitation. However, due to the similar spectral signatures of most mangrove species, accurately mapping mangrove species remains an ongoing challenge. Unmanned aerial vehicles paired with light detection and ranging sensors (UAV-LiDAR) have the potential to increase separability between mangrove species by providing vertical structure information with high flexibility and low cost and could be complementary to optical imagery. In this study, we combined UAV-LiDAR and Sentinel-2 data to classify mangrove species in two representative mangrove forests in China. Moreover, an architecture diagram of important features for discriminating mangrove species was first constructed using a modified recursive feature elimination method and a proposed step-by-step method, including hierarchical clustering, scatterplots, and boxplots. This architecture diagram could indicate the specific role of each feature (or group) in demarcating specific mangrove species and help other mangrove studies prepare candidate features. The combined UAV-LiDAR and Sentinel-2 data produced the highest overall accuracies of 85.60% and 91.61% in the Dongzhaigang National Nature Reserve and Qinglangang Provincial Nature Reserve areas, respectively, compared to those of the Sentinel-2 (80.00% and 80.42%, respectively) and UAV-LiDAR (77.60% and 75.52%, respectively) data alone by using the novel canonical correlation forest algorithm. Important LiDAR features included metrics describing the top, bottom, and overall (e.g., Hstd and GM2nd) morphological characteristics of the mangrove canopy, which characterized the vertical stratification and shape of the canopy. The added value of the UAV-LiDAR data mainly lies in improving the separability between shrub-like mangroves and arbor mangroves that had similar spectral signatures, such as A. marina and R. stylosa. The point density might have little effect on feature selection and classification accuracy, while 10% of the initial point density (∼10 pts/m2) could produce similar accuracy (differences less than 0.5%). This study suggests that the combined UAV-LiDAR and Sentinel-2 data provide a cost-efficient and accurate approach for mangrove species classification.

Mangrove species classification with combination of WorldView-2 and Zhuhai-1 satellite images

Mangrove species classification with combination of WorldView-2 and Zhuhai-1 satellite images

Combining UAV-based hyperspectral and LiDAR data for mangrove species classification using the rotation forest algorithm

Accurate and timely monitoring of mangrove species information is crucial for precise management and practical conservation. Conventional hyperspectral techniques employed in mangrove monitoring are often limited to achieve the fine classification of mangrove species, due to the low spatial resolution of space-borne images and the high cost of airborne images. Moreover, using the spectral information alone is not adequate for fine-scale classification of mangrove species in complex ecosystems, because the spectral discriminability of mangrove species is generally restricted by complex canopy structures. To address these limitations, this study proposes a novel mangrove species classification method that integratively uses unmanned aerial vehicle (UAV)-based Nano-hyperspec hyperspectral imagery, light detection and ranging (LiDAR) data, and the rotation forest (RoF) ensemble learning algorithm. The proposed method was tested in China’s largest artificially planted mangroves, Qi'ao Island. First, we extracted spectral features from UAV-based hyperspectral data and structural information from LiDAR data; then we utilized the RoF algorithm to classify mangrove species based on the spectral and structural features and compared with two other popular ensemble learning algorithms, namely random forest (RF) and logistic model tree (LMT). Results showed that the combined hyperspectral and LiDAR data produced satisfactory results for all three classifiers with overall accuracy (OA) higher than 95%, and the proposed method achieved the highest OA of 97.22% and Kappa coefficient of 0.9686. Our study proved that incorporating the canopy height information can improve the classification accuracy, with the OA and Kappa coefficient being 2.43% and 0.0274 higher than using the original spectral bands alone, respectively. It is also found that the RoF algorithm is more accurate and stable in classifying mangrove species than those of RF and LMT. These findings indicated that the proposed approach could achieve fine-scale mangrove monitoring and further facilitate mangrove forest restoration and management.

Mapping multi-layered mangroves from multispectral, hyperspectral, and LiDAR data

Understanding species distribution and canopy structure of mangrove forests is imperative for flora and fauna conservation in mangrove habitats. However, most mangrove studies focused on the top canopy layer without exploring the vertical structure of mangroves. This paper presents multi-layered mangrove mapping which considered both overstory and understory detection and species classification using multispectral WorldView-3 (WV-3) data, airborne hyperspectral images (HSI), and LiDAR point cloud. First, LiDAR returns were stratified into the overstory and understory by analyzing the profile of return height, which helped understand the vertical structure of the mangrove stands. Second, three classification algorithms Random Forest (RF), Support Vector Machine (SVM), and Convolutional Neural Network (CNN) were compared by applying WV-3, HSI, LiDAR data, and their combinations to map seven vegetative species. Feature selection was conducted to identify important features and the optimal feature size prior to classification tasks. The measured and estimated understory canopy heights reached a high correlation coefficient of 0.71, which demonstrated the effectiveness of using LiDAR data and the proposed procedure to stratify multi-layered canopies. The combined HSI and LiDAR data produced satisfactory results by the three classifiers with overall accuracy (OA) varying from 0.86 to 0.88. And the species was also accurately mapped by integrating WV-3 and LiDAR data using both RF and SVM algorithms with OA attaining between 0.84 and 0.86. The results of this study highlight that (1) LiDAR data provided superior information to map the vertical structure of multi-layered mangroves, which provided valuable information to classify single-layered and dual-layered Kandelia obovata with understory beneath; (2) the combination of spectral and LiDAR features improved mangrove species classification; (3) and species mapping results derived from combined datasets appeared to be more influential by LiDAR features when using RF and SVM, but spectral features played a more important role in CNN.

Mangrove species classification with UAV-based remote sensing data and XGBoost

Mangrove species classification with UAV-based remote sensing data and XGBoost

EXPLORING THE CAPABILITIES OF COMBINING THE SENTINEL-2 MSI DATA AND HIGH RESOLUTION GOOGLE EARTH IMAGE FOR MAPPING MANGROVE SPECIES

Abstract. Knowledge gained about the mangrove species mapping is essential to understand mangrove species development and to better estimate their ecological service value. Spectral bands and spatial resolution of remote sensing data are two important factors for accurate discrimination of mangrove species. In this study, mangrove species classification in Shenzhen Bay, China was performed by using Sentinel 2 (S2) Multi Spectral Instrument (MSI) data and Google Earth (GE) high resolution imagery as data sources and their suitability in mapping mangrove forest at a species level was examined. In the classification feature groups, the spectral bands were from the S2 MSI data and the textural features were based on GE imagery. The SVM classifier was used in mangrove species classification processing with eight groups of features, which were based on different S2 spectral bands and different GE spatial resolution textural features. The highest overall accuracy of our mapping results was 78.57% and the Kappa coefficient was 0.74, which indicated great potential of using the combination of S2 MSI and GE imagery for distinguishing and mapping mangrove species.

Object-Oriented Mangrove Species Classification Using Hyperspectral Data and 3-D Siamese Residual Network

Mangrove species classification is of particular importance for coastal conservation and restoration. However, it is challenging to distinguish species-level differences with limited training data. In this letter, we propose an object-oriented classification method for mangrove forests by using the hyperspectral image (HSI) and the 3-D Siamese residual network. First, superpixel segmentation is utilized to obtain objects with various shapes and scales. Second, 3-D patches of each object are extracted from the original HSI, and those patches containing training samples are adopted to pairwise train the network. The 3-D spatial pyramid pooling (3-D-SPP) is added in the network to extract features in multiple scales. Finally, the abstract features of test samples are learned by the trained network, and the labels are determined by the nearest neighbor classifier within the metric space. Experiments on real mangrove hyperspectral data demonstrate the effectiveness of the proposed method in species classification of mangroves.

Combination of Google Earth imagery and Sentinel-2 data for mangrove species mapping

Knowledge gained about mangrove species mapping is essential to understand mangrove species’ development and to better estimate their ecological service value. Spectral bands and spatial resolution of remote sensing data are two important factors for accurate discrimination of mangrove species. Mangrove species classification in Shenzhen Bay, China, was performed using Sentinel-2 (S2) multispectral instrument (MSI) data and Google Earth (GE) high-resolution imagery as data sources, and their suitability in mapping mangrove forest at a species level was examined. In the classification feature groups, the spectral bands were from the S2 MSI data and the textural features were based on GE imagery. The support vector machine classifier was used in mangrove species classification processing with eight groups of features, which were based on different S2 spectral bands and different GE spatial resolution textural features. The highest overall accuracy of our mapping results was 78.57% and the kappa coefficient was 0.74, which indicated great potential for using the combination of S2 MSI and GE imagery for distinguishing and mapping mangrove species.

Incorporating the Plant Phenological Trajectory into Mangrove Species Mapping with Dense Time Series Sentinel-2 Imagery and the Google Earth Engine Platform

Information on mangrove species composition and distribution is key to studying functions of mangrove ecosystems and securing sustainable mangrove conservation. Even though remote sensing technology is developing rapidly currently, mapping mangrove forests at the species level based on freely accessible images is still a great challenge. This study built a Sentinel-2 normalized difference vegetation index (NDVI) time series (from 2017-01-01 to 2018-12-31) to represent phenological trajectories of mangrove species and then demonstrated the feasibility of phenology-based mangrove species classification using the random forest algorithm in the Google Earth Engine platform. It was found that (i) in Zhangjiang estuary, the phenological trajectories (NDVI time series) of different mangrove species have great differences; (ii) the overall accuracy and Kappa confidence of the classification map is 84% and 0.84, respectively; and (iii) Months in late winter and early spring play critical roles in mangrove species mapping. This is the first study to use phonological signatures in discriminating mangrove species. The methodology presented can be used as a practical guideline for the mapping of mangrove or other vegetation species in other regions. However, future work should pay attention to various phenological trajectories of mangrove species in different locations.

Combining GF-2 and RapidEye satellite data for mapping mangrove species using ensemble machine-learning methods

ABSTRACTMangrove forests are important constitutions for sustainable development of coastal ecosystems, and they are often mapped and monitored with remote sensing approaches. Satellite images allow detailed studies of the distribution and composition of mangrove forests, and therefore facilitate the management and conservation of the ecosystems. The combination of multiple types of satellite images with different spatial and spectral resolutions is helpful in mangrove forests extraction and mangrove species discrimination as it reduces sampling workload and increases classification accuracies. In this study, the 1.0-m-resolution Gaofen-2 (GF-2) and the 5.0-m-resolution RapidEye-4 (RE-4) satellite images, acquired in February 2017 and November 2016 respectively, were used with ensemble machine-learning and object-oriented methods for mangroves mapping at both the community and species levels of the Qi’ao Island, Zhuhai, China. First, the mangroves on the island were segmented from the GF-2 image on a large scale, and then they were extracted combining with their digital elevation model (DEM) data. Second, the GF-2 image was further processed on a fine scale, in which object-oriented features from both the GF-2 and RE-4 images were extracted for each mangrove species. Third, it is followed by the mangrove species classification process which involves three ensemble machine-learning methods: the adaptive boosting (AdaBoost), the random forest (RF) and the rotation forest (RoF). These three methods employed a classification and regression tree (CART) as the base classifier. The results show that the overall accuracy (OA) of mangrove area extraction on the Qi’ao Island with the auxiliary data, DEM, achieves 98.76% (Kappa coefficient (κ) = 0.9289). The features extracted by the GF-2 and RE-4 images were shown to be beneficial for mangrove species discrimination. A maximum improvement in the OA of approximately 8% and a κκ of approximately 0.10 were achieved when employing RoF (OA = 92.01%, κ = 0.9016). Ensemble-learning methods can significantly improve the classification accuracy of CART, and the use of a bagging scheme (RF and RoF) is shown as a better way to map mangrove species than adaptive boosting (AdaBoost). In addition, RoF performed well in mangrove species classification but it was not as robust as the RF, whose average OA and κκ were 80.59% and 0.7608, respectively, while the RoF’s were 77.45% and 0.7214, respectively, in the 10-fold cross-validation.

Continuous Wavelet Analysis of Leaf Reflectance Improves Classification Accuracy of Mangrove Species

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.